Method of network-based identification for drug action and/or synergy effect of medicine combination

ABSTRACT

The invention provides a network-based method for confirming drug action (drug effect, synergistic reaction). The method be carried out by mapping a first drug genes/gene products subset and a second genes/gene products subset in a gene network. The second genes/gene products subset can be a second drug genes/gene products subset or a biological process genes/gene products subset. The invention also provides a tool for pre-clinical drug screening.

FIELD OF THE INVENTION

The present invention relates to a method of network-basedidentification for drug action (NIDA) and/or synergy effect of medicinecombination.

BACKGROUND OF THE INVENTION

Determination of action of medicine is one of the most important basictasks in medicine research and production, and it has a plurality ofaspects, among which a basic one is determination of the treatmenteffect of a medicine.

Some traditional medicine (particularly Chinese traditional medicine)has good treatment effects on relevant diseases. For example, atraditional Chinese medicine recipe called “Qingluoyin” (QLY) havingkuh-seng, caulis sinomenii, golden cypress, and poison yam as its mainingredients has good effect in treating angiogenesis-related diseases,such as arthritis deformans. But QLY has complicated components, withkuh-seng having 54 known components, caulis sinomenii having 45, goldencypress having 46, and poison yam having 30.

Kuh-seng contains Matrine; Sophoridine; Isomatrine; 7,11-Dehydromatrine;Sophocarpine; Isosophocarpine; Sophoramine; Δ⁷-Dehydrosophoramine;Sophoranol; 9a-hydroxysophoramine; 5a,9a-dihydroxymatrine; Oxymatrine;N-oxysophocarpine; Sophoranol-N-oxide; N-methylcytisine; Rhombifoline;Lupanine; Anagyrine; Baptifoline; mamanine; Kuraramine; Isokuraramine;Isoanhydroicaritin; Kushenol C; Kushenol G; Isoxanthohumol;Nor-kurarinone; Kurarinone; Iso kurarinone; Kurarinol; Neokurarinol;Nor-kurarinol; Sophoraflavanone; Kushenol A; Kushenol B; Kushenol E;Kushenol F; Kushenol H; Kushenol I; Kushenol J; Kushenol K; Kushenol L;Kushenol M; Kushenol N; Formononetin; Kushenol O; Kuraridin;Kuraridinol; Kushenol D; Trifolirhizin; Kushenin; Maackiain.

Caulis sinomenii contains 8-Oxocanadine; (−)-8-Oxotetrahydropalmatine;Cheilanthifoline; Acetamide, N-(1,7-dimethoxy-2-phenanthrenyl)-(9CI);Caffeine; 1,7-Dimethylxanthine; Acutupyrrocoline; (+)-menisperine;(+)-laurifoline; Dehydrodiscretine; Epiberberine; Palmatine;Menisdaurilide; Aquilegiolide; 2(4H)-Benzofuranone,5,6,7,7a-tetrahydro-6-hydroxy-, (6R-trans)-; (+)-Dihydroaquilegiolide;8,14-dihydrosalutaridine; Stepharanine; Bianfugenine; Sinomendine;Magnoflorine; Salicylal dehyde; palmitic acid;6H-Dibenzo[de,g]quinoline-6-carboxaldehyde,4,5-dihydro-1,2-dimethoxy-(9CI); (−)-stepholidine; Liriodenine;Allantoin; aristolochic acid; stepharine; N,O-diace-tylmichelalbine;N-acetylstepharine; Michelalbine; acutumidine; dl-Syringeresinol;Syringaresinol; sinoacutine; isosinomenine; (−)-□-Sitosterol;stigmasterol; tuduranine; sinactine; bisinomenine; sinomenine(discovered by Ishiwara); Diversine (discovered by Taguchi).

Golden cypress contains phellodensin G; phellodenol D; phellodenol E;obacunone; obaculactone; berberine; Palmatine; Daucosterol;Cyclohexanecarboxylic acid;3-acetyl-3,4-dihydro-5,6-dimethoxy-1H-2-benzopyran-1-one;quercetin-3-O-□-D-galactoside; Dihydrokaempferol; Phellochinin A;(E,E,Z)-N-(2-Methylpropyl)-2,4,8-tetradecatrienamide;N-methylflindersine; Melianone; Phellochin; (−)-Syringaresinoldiglucoside;□-D-Glucopyranoside,2,6-dimethoxy-4-[tetrahydro-4-[(4-hydroxy-3,5-dimethoxyphenyl)methyl]-3-(hydroxymethyl)-2-furanyl]phenyl,[2R-(2□,3□,4□)]-(9CI); Cyclohexanecarboxylic acid; Cathin-6-one;Friedelin; 5-Cyclodecen-1-ol,4,10-bis(methylene)-7-(1-methylethyl)-(9CI); Niloticin;dihydroniloticin; niloticin acetate; Hispidol B; Piscidinol A; CneorinNP36; Sylvapine A, □-Pinene; □-Geraniolene, □-Myrcene; Cyclohexene,Limonene; NSC 319644, cis-Carveol; trans-Carveol; (+)-Carvone;trans-Limonene oxide; cis-Limonene oxide; □-Elemene; Menisperine;phenyllodendrine; ferulic acid; adenosine; Noroxyhydrastinine;jatrorrhizine; Berbithine; Anhydroberberilic acid; and

Poison yam containsspongiosin B, Diospongin B; spongiosin C, DiosponginC; Piperitol; Sesaminone; hypoglaucin G; Dioscin; gracilin;-D-Glucopyranoside; (1R)-1-ethenylhexyl; 6-O-□-L-arabinopyranosyl-(9CI);-D-Glucopyranoside,(3□,22□,25R)-26-(□-D-glucopyranosyloxy)-22-hydroxyfurost-5-en-3-yl;2-O-(6-deoxy-□-L-mannopyranosyl)-(9CI); □-D-Glucopyranoside,(3□,22□,25R)-; 26-(□-D-glucopyranosyloxy)-22-; hydroxyfurost-5-en-3-ylO-6-deoxy-□-L-;mannopyranosyl-(1□2)—O—[6-deoxy-□-L-mannopyranosyl-(1□4)]-(9CI);Hypoglaucin; Hspongipregnoloside A; Spongipregnoloside B;Spongipregnolosides C; Spongipregnolosides D; □-D-Glucopyranoside,(1R)-1-ethenylhexyl 6-O-□-L-arabinopyranosyl-(9CI); Orbiculatoside B;Dumoside; Trigofoenoside D 1; Zizyvoside I; Glycoside D; LilioglycosideR; Protogracillin; Methylprotodioscin; Pregnadienolone,3-O-□-gracillimatriose; Gracilline; Lilioglycoside G; Lilioglycoside D;Prosapogenin A; Collettiside III; Dioscine; Polyphyllin III; Doursterol;Sitogluside; diospongins A; (+)-Lirioresinol B; +)-Syringaresinol.

With conventional trying-testing approach, it is very difficult andtime/money-consuming to determine the effective components from so manyknown components. In addition, as recipes like QLY may relate to verycomplicated pathologic/physiological process, up to now there has beenno suitable method for analyzing the relationships of so numerouscomponents and complicated processes.

Another basic aspect is the determination of synergy effect of medicinecombinations.

Currently, it is a new approach of disease treatment to develop medicinecomposition having synergistic effect. By “synergistic effect” we meanthe phenomenon that the effect of two medicine components administratedtogether is greater than the sum of the effects of the two componentsadministrated respectively. In allowed Chinese Patent ApplicationCN200610114226.X, which was filed by the assignee of the presentapplication and which is incorporated in full text herewith byreference, a medicine composition having synergistic effect wasdisclosed. However, in the field of medicine development, determinationof extent of synergistic effect between medicine components is basicallyachieved by experiments in vivo and/or in vitro; due to the huge numberof possible combinations and complexity of experiment process, high costand long time were needed for such experiments.

The above are merely two examples of determination of medicine action,which is definitely not limited to the two examples. For example, italso include determining side-effects and etc. of medicines.

Moreover, although the above examples relate to traditional Chinesemedicines, the method of the present invention is not limited to anyspecific type of medicine and is obviously applicable to chemicalmedicines, biological medicines, composite medicines and so on.

SUMMARY OF THE INVENTION

The present inventors, through extensive research, study and experiment,have established a method for determining medicine action on the basisof gene network.

The present inventors have realized that the features of an actionprocess of a medicine (such as an effect to a disease, a side-effect,synergy effect with another medicine, etc.) are related to thecharacteristics of distribution of a subset of genes corresponding tothe medicine in a gene network. More specifically, such characteristicsof distribution include one or more topological attributes of saidsubset of genes with respect to another subset of genes.

Said another subset of genes can be:

-   -   a subset of genes corresponding to a bioprocess; or    -   a subset of genes corresponding to another medicine.

According to an embodiment of the invention, in determining thetreatment effect of a medicine to a disease, by determining one or moretopological attributes of a subset of genes corresponding to themedicine with respect to a subset of genes corresponding to said diseasein a gene network, effect of said medicine on said disease can bedetermined.

According to another aspect of the invention, in determining the synergyeffect of a first medicine and a second medicine, by one or moredetermining topological attributes of a subset of genes corresponding tothe first medicine with respect to a subset of genes corresponding tothe second medicine in a gene network, the synergy effect can bedetermined.

According to an aspect of the invention, there is provided a method fordetermining a medicine action on the basis of a gene network,characterized by comprising:

determining at least one network topological attribute of a first subsetof genes/gene products with respect to a second subset of genes/geneproducts in a gene network,

where

said first subset of genes/gene products includes genes/gene productsrelating to a first medicine,

said second subset of genes/gene products include genes/gene productsrelating to at least one of the following:

-   -   a second medicine, and    -   a bioprocess.

According to an aspect of the present invention, for a bioprocess of adisease under consideration, the genes and/or gene products relating toan action of a medicine and the genes and/or gene products relating tosaid bioprocess are mapped to a gene network, so that extent of actionof the medicine on the bioprocess is quantitively determined quickly andaccurately at low cost. The method of the invention is suitable forlarge scale and high efficiency screening and determination of medicineactions.

According to an embodiment of the present invention, a method isprovided for determining synergistic effect of a medicine combinationincluding two medicines relating to a disease on the basis of a genenetwork and medicine-effective genes, comprising:

determining a synergy factor (ST_(1,2)) of said two medicines;

determining a medicine similarity factor (AS_(1,2)) of said twomedicines; and

determining extent of synergistic effect of said two medicines accordingto a product of said synergy factor and said similarity factor.

In an aspect of the present invention, a method is provided forconstructing a disease-related gene network.

In a further aspect of the present invention, extent of synergy effectof two medicines on a given disease is quantitively determined in thecontext of a gene network relating to said disease by mapping effectivegenes and/or gene products relating to each of the two medicines to saidgene network.

The advantages of the present invention include:

1) the method of the present invention can be implemented on a computeroperating Perl; and,

2) the present invention provides an effective approach for pre-clinicdetermination of extent of synergy of medicine combinations.

The present invention has good prospect for applications in medicinedevelopments. On the one hand it is applicable in quantitivedetermination of the extent of effect of a medicine on a givedisease-related bioprocess (Network-based Assessment for Drug Action),and on the other hand it is applicable in quantitive determination ofextent of synergy effect of two medicines on a given disease(Network-based Identification of Multicomponent Synergy). The presentinvention provides a strong and inexpensive tool for pre-clinicscreening and determination of medicine actions and/or synergy effectsof medicine combinations.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1 a-1 e show experiment data of each of five pairs of medicines ofsinomenine and matrine (SM), sinomenine and honokiol (SH), sinomenineand luteolin (SL), sinomenine and quercetin (SQ), sinomenine andpaeoniflorin (SP); and FIG. 1 f shows scores of maximum inhibition ratio(MIIR) indicating extent of synergy.

FIGS. 2 a-2 b are diagrams for illustrating robustness of the method ofaccording to embodiments of the present invention with respect tomedicine-related gene collections and gene networks.

FIG. 3 is a flowchart showing the method according to an embodiment ofthe present invention for determining extent of medicine effect based anangiogenesis gene network.

FIGS. 4 a-4 i show experimental results, obtained by the presentinventors, of inhibition by matrine, quercetin, emodin, evodiamine,genistein, and aconitine respectively on endothelial cell migration.

DETAILED DESCRIPTION

Definition of some terms relating to the present invention:

1. a “medicine” can be either a single component medicine (such as atraditional Chinese medicine component, a chemical, a biopharmaceutical,etc.) or a composite component medicine (such as a traditional Chinesemedicine, a traditional Chinese medicine ingredient, Chinese medicineeffective component group, compound group, etc.)

2. “gene network” refers to a network formed by genes or gene products(including biomolecules like protein, product of metabolism, and etc.)through interaction; a “gene network” can be a gene regulatory network,a protein-protein interaction network, signal transduction pathwaynetwork, a metabolic network, OR etc; it can also be a network formed bya combination of two or more of these networks.

3. “bioprocess” refers to a pathologic process, a physiological process,a pharmacological process, or a toxic/side-effect process, or otherimportant biological process, or a combined process of a plurality ofthese processes.

4. “gene product” refers to RNA, protein, product of metabolism, or etc.

Principally, a method of network-based identification for drug actionaccording to an embodiment of the present invention is based on tworeasonable considerations. First, for an process of a medicine actionand another process (which for determination of medicine action shouldbe a bioprocess such as pathologic/physiological process concerned,while which for determination of synergy effect of a medicinecombination should be a process of action of the other medicine(s) inthe combination), the subsets of genes/gene products corresponding tothese two processes respectively should not be too far away from eachother in a gene network concerned, for otherwise the interaction betweenthe two processes must be weak and could not lead to a treatmenteffect/synergy effect. Second, the extent of a treatment effect or asynergy effect of a medicine(s) is closely related to the importance ofcorresponding genes/gene products in the gene network, that is, the moreimportant of the genes/gene products relating to a medicine action arein their network, the more remarkable the corresponding effect of themedicine is. The importance of a gene/gene product is measured by thecenterness of its corresponding node in the gene network. Based on theabove considerations, the present inventors have developed a method formeasuring medicine action and determining treatment and/or synergyeffect of a medicine(s) based on network topology.

“subset of genes/gene products relating to a bioprocess”: a subset ofgenes/gene products relating to a bioprocess can be obtained from publicdatabase, such as Gene Ontology (GO) (http://www.geneontology.org/), orfrom biological experiments and/or bioinformatical computations.

According to the definition by GO, a “biological process” refers to anordered assembly of one or more molecular functions. GO has been widelyused in life science, particularly bioinformatics, and has establishedassociations among concepts in the fields of biology and unitedfundamental knowledge, and is regarded as a unification tool of biology.GO includes three relatively independent sections: bioprocess, molecularfunction and cell component. The three sections completely describe thebiological features of gene products, and bioprocess is its primarysection.

For example, one can first obtain the subsets of genes/gene productscorresponding to endothelial cell migration from biological experimentsor database like GO (http://www.geneontology.org/), and obtain subsetsof genes/gene products which a candidate medicine acts on under variousconditions by literature collection and literature mining on such asCNKI and PubMed, docking, and biological experiments, and etc.

Determination of gene subset relating to a bioprocess will be furtherexplained below with respect to specific examples.

“genes/gene products relating to a medicine” and “subset of genes/geneproducts relating to a medicine”: determination (collection) ofgenes/gene products relating to a medicine is a work to be carried outprior to the implementation of the method according to the presentinvention. Specifically, it can be carried out by reading literatures onmedicine research and collecting information on genes/gene productsrelating to a medicine action. Relevant literatures can be downloadedfrom sources like PubMed(http://www.ncbi.nlm.nih.gov/sites/entrez?db=PubMed) or CNKI(http://www.cnki.net/).

In this way, for each medicine, a group of corresponding genes can beobtained, which is a “subset of genes/gene products relating to amedicine”. Determination of subset of genes/gene products relating to amedicine will be further explained later in this specification withrespect to specific embodiments.

Since the method of the present invention has good robustness, itsrequirements on the accuracy of determination of medicine-related genesor gene products and/or accuracy of determination of bioprocess-relatedgenes or gene products are not high. This is a remarkable advantage ofthe method of the present invention.

For a medicine with composite components, a gene/gene product isdetermined as belonging to a subset of genes/gene products relating tothe medicine whenever the gene/gene product is related to one of thecomposite components. “gene network”

An important aspect of the present invention is the determination of theattributes of the topological relationship of two (or more) subsets ofgenes/gene products in a gene network. The “gene network” can be (but isnot limited to) one of the following networks:

-   -   a specially constructed gene network, such as a gene network        especially constructed with regard to a specific disease or        pathologic/physiological/pharmacological/toxic/side-effect        process or etc.,    -   all network relationships existing in a public database of        protein-protein interactions (such as HPRD), i.e., HPRD global        network;    -   all network relationships existing in a public database of        pathways (such as KEGG), i.e., KEGG global network;    -   and etc.

The above-mentioned gene network with regard to a specific disease canbe constructed using, for example, LMMA-BioNet biological networkconstruction software co-developed by the present inventors (see Shao LI, Ningbo Zhang, Lijiang W U, LMMA-BioNet Biological NetworkConstruction Software, Chinese Software Copyright Reg. No. 2008SRBJ0202;Reference: Li S, Wu Zhang Z Q. Constructing biological networks throughcombined literature mining and microarray analysis: a LMMA approach.Bioinformatics 2006,22: 2143-2150), which includes: downloadingabstracts of related literatures containing the disease in theirkeywords from PubMed; finding genes contained in the downloadedabstracts by matching with dictionary of human genes downloaded fromHUGO website (http://www.genenames.org/); and, searching forrelationships between two genes in the databases of KEGG(http://www.genome.ip/kege) and HPRD (http://www.hprd.org/); determiningthat an interaction exists between two genes when a relationship isfound existing between the two genes. Relationship of functionalinteraction or physical interaction between two genes can also berealized using one of a plurality of other approaches, such as“concurring”, “co-expression of genes”, “gene co-regulation”, and etc.With the above approach, construction of disease-related gene networkcan be carried out by a machine (computer); it is not a necessary partof the method of network-based identification for drug action and/orsynergy effect of medicine combination according to the presentinvention; rather, it can be an optional part of the method of thepresent invention; that is, construction of disease-related gene networkcan be either a preparative work of the method of the present inventionor a part of the work carried out by the method of the invention.

It is shown by the experiments and computations carried out by thepresent inventors that the final results are not substantially affectedby the use of different gene network among the above-mentioned threenetworks or by adding or deleting edges in the used network, indicatingthat the method of the invention has very good robustness, which is aremarkable advantage of the present invention.

Example of Construction of a Disease-Related Gene Network

As an exemplary implementation, the inventors made two independent textfiles to store the gene relationships in HPRD (http://www.hprd.org/) andKEGG (http://www.genome.jp/kegg/) respectively, with each line in thefiles representing one known gene relationship. For a pair of genes ofinterest, matching can be carried out in each of the two filesrespectively; and, whenever it is found that the two genes appear in onesame line in any of the two files, it is determined that an interactionrelationship exists between the two genes, i.e., the two genes have aneighboring relationship in their gene network.

As an equivalent but possibly more efficient approach, however, thematching search was carried out in the other direction, i.e., for eachpair of genes in HPRD and KEGG databases, it was determined whether bothof the genes in the pair appeared in an established subset ofdisease-related genes/gene products, thus constructing a disease-relatedgene network.

In an alternative embodiment, the global gene network of HPRD and/orKEGG database can be used as the gene network for implementing themethod of the present invention. The effects of the present inventioncan be realized using different gene networks (as will be explainedlater).

Embodiment 1 Determining Synergy Effect of a Medicine Combination

According to an embodiment of the present invention, synergy factorST_(1,2) of a medicine combination is determined as:

$\begin{matrix}{{ST}_{1,2} = {\frac{1}{2} \times \begin{pmatrix}{\frac{\sum\limits_{i}{{{IP}_{1}(i)} \times {\exp \left( {- {\min \left( d_{i,j} \right)}} \right)}}}{\sum\limits_{i}{{IP}_{1}(i)}} +} \\\frac{\sum\limits_{j}{{{IP}_{2}(j)} \times {\exp \left( {- {\min \left( d_{j,i} \right)}} \right)}}}{\sum\limits_{j}{{IP}_{2}(j)}}\end{pmatrix}}} & {{Formula}\mspace{14mu} (1)}\end{matrix}$

where:

−IP₁(i) and IP₁(j) are factors of node importance of network nodes i andj respectively. According to an embodiment of the present invention,factor of node importance IP of a node is determined by using principalcomponents analysis (PCA) to synthetize a NodeRank centerness, aBetweenness centerness, and/or a Closeness ceneterness of the node.Detailed description of use of principal components analysis (PCA),NodeRank centerness, Betweenness centerness, Closeness ceneterness andtheir determination will be given later in the present specification;

d_(i,j) is the shortest network distance/path from node i to node j, andd_(j,i) is the shortest network distance/path from node j to node i.Calculation of the shortest network distance will be explained later inthe present specification.

According to a further embodiment of the present invention, optionally,medicine similarity and medicine similarity coefficient (AS_(1,2)) areintroduced in addition to disease-related gene network topologicalinformation; the medicine similarity coefficient (AS_(1,2)) is used toweight the synergy factor obtained. A basic idea of medicine similarityis that the greater the similarity between diseases treated by twomedicines, the greater the possibility that the two medicines havesynergy effect when they are used to treat a relevant disease. Therelation of correspondance of a medicine and relevant diseases can beretrieved, according to genes, from data in OMIM (Online MendelianInheritance in Man, www.ncbi.nlm.nih.gov/omim). Specifically, as long asone gene among the genes corresponding to a medicine is within a set ofgenes that lead to a disease, we will determine that a relation ofcorrespondance exists between this medicine and this disease. In thisway, a first medicine corresponds to a first group of diseases, and asecond medicine corresponds to a second group of diseases, and we obtain

${{AS}_{1,2} = \frac{\sum\limits_{i,j}P_{i,j}}{N}},$

where Pi,j is the similarity factor between disease i in the first groupof diseases and disease j in the second group of diseases, and N is forall pairs of diseases between the first and second groups of diseases.

A method for determining Pi,j was provided by Van Driel, M. A., et al.in “A Text-Mining Analysis of the Human Phenome”, Eur. J. Hum. Genet.2006, 14, 535-542, which included mapping each syndrome item in OMIMdatabase to a set of (0,1) vectors in accordance with standardvocabulary. Thus, two given syndromes, in accordance with theirdescription in words in OMIM database, can be mapped to two sets of(0,1) vectors v1 and v2 of standard vocabulary. Then, the Pi,j value isdetermined as the cosine value cos θ of the angle between the twovectors

${\cos \; \theta} = {\frac{\overset{\_}{v\; 1} \cdot \overset{\_}{v\; 2}}{{\overset{\_}{v\; 1}}{\overset{\_}{v\; 2}}}.}$

Using this method, the present inventors had determined a AS_(1,2) valueof 0.17075 for sinomenine+matrine, a AS_(1,2) value of 0.15897 forsinomenine+honokiol, and a AS_(1,2) value of 0.17050 forsinomenine+luteolin.

From the above, a formula for synergistic effect scoring of a method ofnetwork-based identification of multicomponent synergy according to thepresent invention is:

S _(1,2) =ST _(1,2) ×AS _(1,2)

Then, calculation of network shortest distances (paths) is performed.Floyd Algorithm, which is known as the most effective way for doing thiscalculation, is used in a preferred embodiment of the present invention.Other algorithm for calculating network shortest distances, however, canbe used. All such alternative embodiments are within the scope of thepresent invention.

Then, node importance is determined. In an embodiment of the presentinvention, a value of importance factor IP of each node is determined byperforming principal components analysis (PCA) at lease one of aNodeRank value, a Betweenness value, and a Closeness value of each node(definitions and algorithms of these values are described later in thepresent specification); such principal components analysis (PCA) will bedescribed later in the specification, wherein minimum valuenormalization is performed on any of the three values used.

Calculation of synergy factor is performed in accordance with Formula(1) given above.

Then, a ranking is made for the results based on the above synergyfactor. score and ranking of a medicine combination indicates the extentof synergy in treating the disease concerned.

Calculation of matrix of shortest distances/paths in accordance with thepreferred embodiment of the present invention

Use of Floyd Algorithm

In Floyd Algorithm, adjacency matrix A=[a(i,j)]n×n of a graph isrecursively updated up to n times; that is, from matrix D(0)=A, matrixD(1) is constructed in accordance to a formula; then, D(2) isconstructed from D(1) using the same formula; . . . ; and matrix D(n) isconstructed from D(n−1) using the same formula. The element of the i-throw and the j-th column of matrix D(n) is the length of the shortestpath from the i-th vertex to the j-th vortex, and D(n) is called thepath matrix of the graph. Also, a successor node matrix is introduced torecord the shortest path of two points.

The above-mention matrix series can be obtained by iterative method;specifically:

D(i,j):dij(k);

Path(i,j): corresponding to successor point of i on path of d(i,j)(k),with a final value of a successor point of i on the shortest path from ito j;

inputting weighted adjacency matrix A=[a(i,j)]n×n;

1) initiating values

for all i,j,d(i,j)=a(I,j); when a(i,j)=infinite, path(i,j)=0, otherwisepath(i,j)=j; k=1;

2) updating d(i,j), path(i,j)

for all i,j, if d(i,k)+d(k,j)>=d(i,j), go to 3); otherwised(i,j)=d(i,k)+d(k,j), path(i,j)=path(i,k), k=k+1, continuing with 3);

3) repeating 2) until k=n+1.

About PCA Method and Related Parameters:

NodeRank

NodeRank is an eigenvector corresponding to the maxiumu eigenvalue ofnetwork-associated matrix. NodeRank value of node A can be determined byformula:

${P(A)} = {\frac{1 - d}{N} + {d{\sum\limits_{v \in L_{v}}\frac{P(v)}{N_{v}}}}}$

where N is the number of all nodes in the network, d is an attenuationfactor and is usually set at 0.85, which indicates uncertainty of edgesin the network, L_(v) is the set of nodes that directly connect node A,and N_(v) is the number of edges of node v.

Betweenness

Betweenness of a node indicates the number of the shortest paths betweenall pairs of nodes which go through said node. Betweenness of a nodeprovides a very good description of the flow rate which the nodepossibly undertakes. The greater the Betweenness of a node is, the moredata packs that flow through the node, indicating that the node is morelikely to be jammed and becomes a bottleneck in the network.

Denoting the number of the shortest paths between any two nodes s and dof a graph as σ_(sd) and denoting the number of the paths in theseshortest paths passing through node w as σ_(sd)(w), the proportion ofthe number of paths in these shortest paths passing through node w tothe number of shortest paths between nodes s and d being

$\frac{\sigma_{sd}(w)}{\sigma_{sd}},$

then Betweenness of node w is defined as:

${C_{B}(w)} = {\sum\limits_{s \in V}{\sum\limits_{{d \neq s} \in V}\frac{\sigma_{sd}(w)}{\sigma_{sd}}}}$

Closeness:

Closeness is a measure of importance of a node. Closeness of a node v isdefined as the inverse of the sum of the shortest paths from the node toeach of all other nodes in the network:

${C(v)} = \frac{1}{\sum\limits_{t \in V}d_{v,t}}$

where V is the set of nodes in the network that communicate to node v,and d_(v,t) is the shortest path from node v to node t.

Principal Components Analysis (PCA):

An practical example of application of PCA in the present invention isgiven below.

Example 1-1

The inventors used the method of network-based identification fordrug/medicine action (also called “NIDA method”) to select medicinecombinations for anti-angiogenesis. 63 candidate medicines were chosen,including 14 medicines of single component and 51 medicines of compositecomponents, which were:

5-fluorouracil, Rapamycin, Vinblastine, Camptothecin, Baicalein, Emodin,Genistein, Honokiol, Indirubin, Luteolin, Matrine, Sinomenine,Paeoniflorin, Quercetin, and

white atractylodes rhizome, rhizoma atractylodis, radix paeoniaerubrathe (unpeeled) root of common peony, monkshood, Ligusticumwallichii, the root bark of the peony tree, the root of red-rootedsalvia, Fructus Ziziphi Jujubae, cortex lycii radicis, Rheum officinale,catechu, the root of fangji (Stephania tetrandra), Poria cocos,monkshood, liquorice, the root of kudzu vine, cassia twig, Polygonummultiflorum, flowers carthami, golden cypress, the rhizome of Chinesegoldthread, Platycodon grandiflorum, Fructus Tribuli, honeysuckle,Forsythia suspensa Vahl, black false hellebore, Akebia quinata Decne,fructus arctii, the root of bidentate achyranthes, Angelica decursiva,ginseng, pseudo-ginseng, subprostrate sophora, Rhizoma Dioscoreae,maythorn, fructus corni, Acorus gramineus Soland, radices trichosanthis,rhizoma gastrodiae, rhizoma smilacis glabrae, radix clematidis, thefruit of Chinese magnoliavine, evodia fruit, realgar, polygala root, therhizome of oriental water plantain, fructus aurantii, Cape jasmine, andgrifola.

The 63 candidate medicines gave 63*(63−1)/2=1953 medicine combinations.

Among these combinations, two had been known as medicine combinationshaving anti-angiogenesis synergistic effect: 5-fluorouracil+Vinblastine,and 5-fluorouracil+Rapamycin. In predications by NIDA method for all the1953 combinations, these two medicine combinations ranked among the topthree in the ranking of synergistic effect (see Table 1), no matterwhich of the three gene networks described above was used. These resultsshowed that the NIDA method of the present invention was effective indetermining medicine combinations with synergistic effect.

TABLE 1 NIDA synergistic effect ranking of 5-fluorouracil + Vinblastineand 5-fluorouracil + Rapamycin in 1953 medicine combinations Synergisticeffect ranking of 5-fluorouracil medicine combinations by NIDA method5-fluorouracil + 5-fluorouracil + gene network Vinblastine RapamycinDisease-specific Angiogenesis 2 3 network network Global network HPRD 12 global network KEGG 2 3 global network

Cell Proliferation Verification on Results of NIDA Method

Using NIDA method of the present invention, the present inventorsdetermined scores of synergistic effect of five medicine combinations:sinomenine+luteolin, sinomenine+quercetin, sinomenine+honokiol,sinomenine+matrine, and sinomenine+paeoniflorin. Further, the presentinventors verified the results of the scores of NIDA method of thepresent invention by cell proliferation verification experiments.

Cell proliferation verification experiments: human umbilical veinendothelial cells (HUVEC) were purchased from Cascade Biologics Culture;M200 culture medium containing 2% low density of fetal bovine serum and5 ng/ml of bFGF was used in cell culture; incubation of culture wascarried out under 37° C. and 5% CO₂ environment; passage culture wascarried out at 1:2 passage ratio using pancreatin/EDTA as assimilationliquid. HUVECs of the 3th-6th generations were used in all theexperiments. Small molecule standard substances of sinomenine, luteolin,quercetin, honokiol, matrine, and paeoniflorin were purchased fromNational Institute for the Control of Pharmaceutical and BiologicalProducts.

Density, dosage, and combination ratio of each small molecule andmedicine combination were designed on the basis of the description ofrelevant references. DMSO hydrotropy was used for sinomenine, luteolin,quercetin, honokiol, and paeoniflorin, while matrine could be directlydissolved in culture medium. Each medicine was preserved under −20° C.environment as a mother liquor with a final concentration of 50 mM.Extent of cell proliferation was evaluated using the Cell Counting KitCCK-8 developed by DOJINDO Laboratories. Specific experiment stepsincluded performing 24 hour cell proliferation, adding two medicinesseparately and in combination into 96 orifices, adding 10 microlitres ofCCK-8 reagent to each of the orifices 48 hours after pharmacologicintervention, and then incubating the 96 orifices for 4 hours under 37°C. followed by OD value measurements. Each group of experiments wasrepeated for three times.

FIGS. 1 a-1 e illustrate experiment data for sinomenine and matrine(SM), sinomenine and honokiol (SH), sinomenine and luteolin (SL),sinomenine and quercetin (SQ), and sinomenine and paeoniflorin (SP),respectively, where each gray line indicates inhibition ratio of themedicine combination concerned to HUVEC cell proliferation (curve ofdosage dependency) and each black line indicates corresponding curve ofBliss additive effect.

Comments: by this study, the inventors considered each low-dosagecombination with an inhibition ratio greater than 70% as an effectivecombination, and the inventors evaluated synergistic effect usingdosages and combination ratios, Bliss additive effect model, and maximuminhibition ratio reported in pharmacal literatures. Whether a medicinecombination had synergistic effect was determined using Bliss additiveeffect model. To further evaluate intensity of synergistic effect, theinventors used formula MIIR=max(IR_(syn)−IR_(add)) to calculate maximumincreased inhibition rate, MIIR, with IR_(syn) and IR_(add) respectivelyrepresenting measured inhibition ratio and added inhibition ratio of themedicine combination concerned.

FIG. 1 f shows score of maximum increased inhibition rate (MIIR) of eachof the five medicine pairs, which indicates extent of synergisticeffect, wherein maximum synergistic effect ratio of each medicinecombination is indicated by a histogram. It can be seen from FIG. 1 fthat MIIR score of each of the five medicine pairs was in consistentwith the ranking of NIDA predication.

Result analysis: by comparing results of cell experiments and those ofNIDA prediction (Table 2), it was shown that ranking obtained fromresults of cell experiments were fully consistent with results ofsynergistic effect intensity determined by NIDA prediction based on genenetwork, so the reliability of NIDA method of the present invention wasverified. Also, using NIDA method of the present invention, theinventors found three sinomenine combinations each having relativelystrong synergistic effect on anti-angiogenesis, which were:sinomenine+matrine (MIIR=26.83%), sinomenine and honokiol (MIIR+22%),and sinomenine and luteolin (MIIR=21%). The medicine combination ofsinomenine and paeoniflorin, which ranked last in NIDA ranking and cellexperiment verification, yielded an MIIR of only 1.86%, indicating thatit had little synergistic effect.

The present inventors further investigated the robustness of NIDA methodof the present invention with respect to the gene network used. Forthis, the present inventors made predictions using each of the threenetworks respectively, which resulted in basically the same outcomes, asshowed in Table 3. In terms of prediction accuracy, results obtained byusing disease-related gene network (angiogenesis network) and HPRDglobal network respectively were better, while result obtained by usingKEGG global network was relatively inferior.

TABLE 2 Prediction scores of synergistic effect of medicine combinationsincluding Sinomenine by NIDA Method based on angiogenesis networkSynergistic effect score by NIDA prediction Medicine NodeRank, Between-Cell combined Betweenness, ness, experiment Ranking with SinomenineCloseness Closeness MIIR value 1 Matrine 0.10923 0.117280 26.83% 2Honokiol 0.10142 0.112270 22% 3 Luteolin 0.10007 0.105980 21% 4Quercetin 0.09835 0.104840 15% 5 Paeoniflorin 0.082148 0.084320 1.86%

TABLE 3 Prediction scores of synergistic effect of medicine combinationsincluding Sinomenine by NIDA Method based on three networks NIDA synergyeffect ranking Medicine combined angiogenesis HPRD global KEGG globalwith Sinomenine network network network Matrine 1 2 1 Honokiol 2 1 3Luteolin 3 3 4 Quercetin 4 4 2 Paeoniflorin 5 5 5

Steps of an Embodiment of NIDA Method:

1. In an embodiment, where angiogenesis network was used, computation ofNodeRank, Betweenness, and Closeness in NIDA method and PCA were carriedout as follow:

(1) The angiogenesis network that the present inventor constructed in amethod of construction of disease-related network as described above wasa connected sub-network containing 1893 gene nodes and 7598 edges. Dataof the angiogenesis network are available and downloadable at:

http://bioinfo.au.tsinghua.edu.cn/member/nzhang/download/angiogenesis.txtwherein all genes are indicated by Entrez GeneID of NCBI.

(2) NodeRank, Betweenness, and Closeness of each of these nodes werecalculated in accordance to the above definitions of these parameters.To ensure comparability among the data, they were normalized by dividingtheir respective minimum. Thus, a 1893*3 matrix (denoted as Matrix A)was obtained. Matrix A is given in the part of this specificationsubtitled “Part I: Noderank-Betweenness-Closeness values of AngiogenesisNetwork”, where the three values contained in the i-th pair ofparentheses are the NodeRank, Betweenness, and Closeness valuesrespectively of the i-th gene listed on webpage:http://bioinfo.au.tsinghua.edu.cn/member/nzhang/download/angiogenesis.txt

(3) PCA (principal components analysis) on the three 1893*1 vectors,which were constructed by the three columns of Matrix A respectively,was conducted in MATLAB. Denoting the final node importance factorvector as IP, then the commands in MATLAB were:

[COEFF, SCORE]=princomp(A); IP=-A*COEFF(: 1)

Of course, we could also choose only two of the three vectors for PCA toobtain the IP value of the node. When only Betweenness and Closenesswere chosen and the then obtained 1893*2 matrix was referred to as C,the corresponding commands in MATLAB were:

[COEFF, SCORE]=princomp(C); IP=-A*COEFF(: 1)

In embodiments of the present invention, the present inventors appliedNIDA method to the combinations of Sinomenine-Matrine,Sinomenine-Honokiol and/or Sinomenine-Luteolin, respectively, in thefollowing way:

(1) First, IP value of a gene relating to a medicine was the IP value ofthe corresponding node (gene) in the disease-related network. IP valuescorresponding to respective genes of sinomenine, matrine, honokiol, andluteolin respectively are given in the parts of this specificationsubtitled “Part II” to “Part V” respectively.

(2) Then, on the basis of matrix of shortest paths of the angiogenesisnetwork, a matrix of shortest paths among genes for each of the medicinepairs Sinomenine-Matrine, Sinomenine-Honokiol and Sinomenine-Luteolinwas obtained, as given in the parts of this specification subtitled“Part VI” to “Part VIII” respectively, where the lines in each of thematrix corresponds to the genes of sinomenine and columns corresponds tothe genes of the other medicine. From this network topology information,the ST value of each of the pair of medicines was obtained (as 0.63971for Sinomenine-Matrine, 0.63800 for Sinomenine-Honokiol, and 0.58692 forSinomenine-Luteolin).

(3) ST value of each of the pairs of medicines was weighted bycorresponding AS value (Sinomenine-Matrine: 0.17075,Sinomenine-Honokiol: 0.15897, Sinomenine-Luteolin: 0.17050) to obtain afinal value of synergy factor S (as 0.10923 for Sinomenine-Matrine,0.10142 for Sinomenine-Honokiol, and 0.10007 for Sinomenine-Luteolin).

(4) These pairs of medicines were ranked according to the above resultsof extent of synergy of each of them.

On Robustness of NIDA Method

1. Robustness with Respect to Random Addition/Reduction ofMedicine-Effective Genes

In collecting medicine-effective genes by reviewing literatures,neglects and/or mistakes in the references can hardly be avoided. Thepresent inventors evaluated the robustness of NIDA method by randomlyincreasing/decreasing the genes relating to medicines, and described therobustness evaluation by comparing Spearman rank pertinence before andafter permutation, and obtained the results as shown in FIG. 2. As theresults shown in FIG. 2 indicated, effect of random addition of genes onresult of NIDA method was not obvious, while effect of random deletionof genes on result of NIDA method was relatively remarkable; however,even when the number of genes was deleted by 50%, the pertinence of NIDAresult to NIDA result without gene deletion still reached 50%. The aboveexperiment outcomes indicated that NIDA method of the present inventionhad strong robustness to collection of medicine-effective genes.

2. Robustness with Respect to Random Increase/Decrease of Gene Network

Construction of gene network may be inaccurate and/or incomplete, andthe ways of construction may be different, leading to difference in thegene networks concerned. To evaluate the effects of such differences onthe results of the NIDA method of the invention, the present inventorsconducted random increase and decrease of the gene network to measurethe robustness of the NIDA method of the invention to gene network,wherein the robustness evaluation was measured by comparing Spearmanrank pertinence before and after the increase/decrease of the network.The result was as shown in FIG. 2 b, which indicates that neither randomincrease of edges in the network nor random decrease of edges in thenetwork had obvious effect on the results obtained by the method of theinvention. This indicated that the NIDA method of the invention had goodrobustness with respect to the gene network.

Part I: Noderank-Betweenness-Closeness Values of Angiogenesis Network(Matrix A)

(28.304,29.846,2.7015);(1.2325,1,1.7449);(24.564,22.378,2.541);(1.2257,1,1.7447);(2.0985,1.0421,1.9812);(1.826,1.0112,2.0015);(2.2416.1,1.6851);(2.0573,1.4359,2.1775);(4.6517,3.0379,1.7682);(3.4778,1.1472,2.3706);(3.9337,1.1581,1.9943);(38.952,33.064,2.8982);(3.3814,2.5698,1.9309);(1.5289.1,1.9501);(1.0724,1,1.9262);(5.4903,3.7287,2.0068);(9.1525,3.625,2.4496);(15.469,5.6757,2.5692);(4.6133,2.6714,2.068);(12.307,4.2026.2.3747);(3.1998,1.1501,1.8779);(6.4805,3.7035,2.1668);(3.0421,1.2686,2.124);(8.0527,1.6802,1.9328);(5.3362,2.4403,2.0612);(5.1311,4.0996,1.9438);(4.5309,1.1234,2.3687);(3.8859,1.1694,1.7156);(1.4309,1.1.6823);(4.1645,1.3791,1.7071);(2.2703,1.3173,2.0826);(1.3369,1.1.7451);(1.9414,1.1.4263);(1.1335,1.2.1322);(14.095,6.4194,2.378);(3.7074,5.548,2.193);(15.486,7.3526,2.6097);(1.0464,1.2.1145);(6.0541,1.2202,1.8854);(2.7165,1.2,1.8628);(6.0701,2.9979,1.4297);(7.0989,2.8667,2.2514);(1.3127,1,1.6808);(1.6399,1.0176,1.7724);(7.0334,2.7518,2.236);(3.2205,1.2792,1.9291);(5.8846,4.11412.2384);(6.1965,2.7663,2.3947);(6.9995,4.4391,2.4193);(17.786,9.0719,2.5815);(1.1212,1,1.9316);(6.2869,2.4204,1.9626);(2.1177,1.0803,2.1609);(10.208,3.6215,2.4799);(3.6106,1.1189,2.1747);(1.2626,1,1.7697);(8.4619,3.3604,2.4358);(1.4636,1,1.6027);(5.5471,4.131,2.3624);(26.389,16.534,2.7565);(2.0032,1.0023,1.9368);(2.2835,1.1076,1.9794);(3.3908,2.309,2.1228);(5.0891,2.0201.2.1728);(1.2489,1,1.7544);(8.3371,6.17,2.2873);(I.4766,1,1.6405);(6.9743,1.6134,2.1103);(3.6729,1.2107,2.1388);(4.0415.1.3411,2.3432);(3.738,1.0147,2.3162);(5.4189,1.4332,2.3259);(5.9447,3.0432,2.3863);(5.2243,3.0499,2.3277);(1.7853,1,1.6639);(1.9881,1,1.2274);(1.3043,1,1.6992);(1.6554,1,1.5048);(2.7575,1.4891,2.2036);(3.2597,1.3797,1.9463);(4.3039,2.9837,2.2072);(2.0251,1.401049,2.02);(4.9476,1.8792,2.1498);(3.3176,1.5054,1.989);(1.3064,1,1.6471);(1.0712,1,2.1151);(12.261,9.3364,2.4158);(2.7549.1.1276,2.0328);(1.0961,1,2.0023);(5.4327,3.1294,2.0444);(1.2562,1,1.7747);(5.1927,3.0036,1.6167);(15.259,12.342,2.3241);(1.5148,1.0015,1.884);(8.7888,3.2494,2.3981);(4.2793,1.1514,2.1529);(4.1271,2.435,2.0737);(3.121,2.0681,2.2454);(5.0179,2.8864,2.2301);(1.291,1,1.6604);(12.273,10.813,2.1331);(1.3765,1,1.6098);(2.7841.1.0205,2.0422);(3.6895,1.5383,2.2457);(1.4859,1,1.6584);(8919,1.0263,2.2511);(1.1883,1.1.8861);(1.6719,1.0024,1.8793);(2.7529,1.1509,1.9721);(831,1.0003,1.6927);(5.3596,3.9649,1.641);(2.0986,1.0067,1.9341);(4.4589,2.5125,1.8868);(2.2027,1.021,2.1557);(1.9325,1,1.4136);(13.408,4.3841,2.3962);(2.2504,1.2352,1.998);(1.6824,1,1.3849);(1.6388,1.039,2.0159);(2.3948,1.1932,1.5444);(2.4824,1.0425,2.0224);(5.0687,2.9388,2.3962);(3.3464,1,1.3014);(15.236,9.7939,2.6184);(1.3734,1,1.6477);(3.975,3.57,1.9301);(16.151,6.5691,2.5419);(2.2352,1.1069,2.0575);(20.889,12.899,2.7054);(1.604,1,1.4132);(7.461,3.8938,2.1731);(1.0741,1,1.7231);(1.365,1,1.6714);(5.6803,3.3867,2.1731);(2.8509,1.0225,2.0195);(4.2876,1.883,2.066);(4.1552,2.2374,2.204);(7.0539,5.7621,2.2205);(4.192,1.6853,2.2444);(3.9513,1.7254,1.9631);(1.441,1,1.272);(5.8707,2.234,2.4228);(2.2236,1,1.6877);(4.1054,3.3029,2.2602);(1.9736,1.006,1.9494);(3.6496,1 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14.7,5.2151,2.3837);(1.9793,1.0083,1.7223);(2.4617,1.1085,2.0991);(2.8012,1.0459,2.1743);(4.0471,1.1856,2.152);(1.2319,1,1.6542);(2.3418,1.0628,1.876);(4.672,1.6564,2.3502);(6.379,1.716,2.4042);(3.421,1.4621,2.214);(4.9404,1.6688,2.3993);(1.5225,1.0063,2.0146);(2.2897,1.0009,1.7823);(2.2837,1.0606,2.1358);(2.1004,1.0208,2.1486);(1.0586,1,1.7816);(2.46,1.0033,2.2059);(1.2766,1,1.6226);(2.3841,1.0165,2.1056);(2.9898,2.531,2.1352);(3.5115,1.104,1.6043);(1.9566,1.0076,2.0806);(2.3315,1.0467,2.1918);(3.7531,2.0401,1.9893);(2.563,1.0234,2.0991);(2.3493,1.0758,1.8025);(3.5299,1.7097,1.6986);(6.5014,3.5724,2.0178);(15.382,6.9569,2.6335);(1.2742,1,1.5724);(16.709,7.9999,2.6642);(2.7444,1,1.6661);(11.816,4.2743,2.2992);(4.585,5.0225.2.2301);(13.585,5.5789,2.458);(16.465,7.8613,2.3003);(3.9742,1.9304,2.2694);(2.7591,1.0031,1.5855);(13.814,7.9858,2.4385);(3.1525,1.9992,1.589);(1.1664,1,1.8384);(5.9544,1.3408,1.9267);(5.629,1.6711,2.355);(3.0533,1.2612,1.7463);(1.974,1.3549,2.2407);(2.2039,1.0117,1.5469);(1.8255,1.1662,2.1612);(5.0858,2.2203,2.2514);(13.185,3.3483,1.9775);(1.687,1.0609,2.1163);(4.4464,2.3913,2.2344);(1.2019,1,1.7396);(1.8291,1.1825,1.9675);(5.6626,2.5579,2.313);(1.3871,1,1.7219);(2.2939,1.5213,2.0881);(1.1867,1,1.8053);(15.022,6.0286,2.4429);(3.5046,2.0108,1.7991);(10.349,6.2855,2.452);(1.1626,1,1.7594);(2.6677,1,1),(1.3266,1,1.7406);(1.1247,1,1.836);(4.6308,1.3386,2.2968);(1.115,1,1.9506);(5.0755,1.4613,1.5006);(1.5494,1.0024,1.9721);(1.7157,1.0318,1.8377);(2.9284,1.6094,1.8138);(2.5492,1.0355,1.9796);(3.2297,1.0001,1.4774);(6.2614,2.5551,2.2189);(2.4627,1.0347,2.2176);(2.0182,1.0026,1.8591);(13.025,3.6169,2.4584);(2.0697,1.2663,1.8866);(4.4103,3.6875,1.7542);(4.5627,1.3171,2.0157);(1.365,1,1.6714);(1.8832,1.0114,2.1112);(1.108,1,1.8628);(10.306,9.9904,2.3327);(3.2803,1.5398,2.3425);(19.231,15.037,2.3066);(1.3975,1,1.6504);(5.7236,4.7679,2.3447);(5.2099,1.246,2.3447);(3.2758,1.1763,1.7635);(2.0739,1.0196,2.0878);(3.243,1.0001,1.3464);(1.1183,1,1.6637);(6.2262,1.5188,2.3784);(7.6596,4.8033,2.3739);(1.9262,1.0191,1.7829);(32.462,21.171,2.6704);( 1.2346,1,1.6432);(14.539,3.8909,2.4588);(5.3195,1.523,2.0055);(5.1065,1.2884,2.3572);(7.9066,5.0651,2.1816);(3.7127,1.1214,2.268);(3.0295,1.4993,1.4513)( 1.6377,1,1.8579);(1.8803,1,1.392);(3.554,2.0019,1.5139);(5.6257,2.1726,2.0124);(2.4056,1,1.212);(4.5024,2.9919,2.2347);(5.0079,1.5246,2.3484);(2.645,1.6283,2.1778);(1.6296,1.0121,1.961);(5.4872,1.0917,1.8956);(4.7031,2.6926,2.1835);(2.5973,1.2037,1.4178);(2.6648,1.0421,1.9814);(2.9186,1.0008,1.8112);(9.9378,6.2505,2.4464);(7.0717,3.8945,2.3349);(3.385,1.2072,2.2595);(1.0712,1,2.1151);(2.3642,1.0449,2.0615);(5.6083,2.6334,2.1325);(39.493,31.203,2.8194);(1.3166,1,1.69);(46.299,36.165,2.7809);(4.6401,1.2976,2.1693);(3.3428,2.0221,2.1959);(1.7758,1.0117,1.9062);(2.3539,1.0003,1.6641);(1.2484,1,1.8438);(28.483,21.546,2.6751);(3.8851,1.0501,1.8328);(1.3549,1,1.6668);(1.8106,1.036,1.8676);(3.0231,1.0132,2.1969);(3.2608,1.0003,1.2277);(1.8187,1,1.5265);(1.4802,1.0125,2.1331);(1.3979,1.0013,2.0657);(7.86713.0571,2.287);(2.329,1.1906,1.827);(3.5314,2.2157,1.7972);(2.3079,1.2401,1.8447);(1.3484,1,1.5452);(2.7147,1.2696,2.3059);(3.6006,1.088,1.9983);(1.4636,1,1.6027);(4.2678,2.5118,1.958);(2.3819,1.0019,2.1529);(2.0487,1,1.7835);(1.2214,1,1.7188);(2.6623,1.1154,2.1756);(5.7962,1.6445,2.0464);(1.8029,1.0003,2.1994);(1.3171,1,1.6398);(8.6269,1.4522,2.3531);(1.5688,1.0068,1.9181);(84075,1.6577,2.3517);(6.0587,3.6758,2.0436);(13.081,9.2426,2.5596);(3.6496,1.0249,2.1841);(4.6101,2.2946,2.3735);(4.4054,1.8208,2.3223);(2.0353,1.6042,1.8095);(1.1036,1,1.9436);(3.0371,1.0551,2.2575);(3.0067,1.0856,1.9983);(2.1959,1.117,2.14);(3.0063,1.2834,2.288);(1.2006,1,1.8835);(3.5091,1.226,1.9856);(3.8147,1.5037,2.2404);(2.7444,1,1.6661);(11.173,5.5676,2.521);(2.8353,1.1343,1.9086);(1.0111,1,1.958);(1.8599,1.0011,2.2069);(7.181,3.3873,2.3119);(8.139,2.5175,2.2387);(11.532,6.273,2.1489);(1.8071,1.0009,2.0464);(1.9594,1.0222,1.9608);(3.5982,1.5567,1.9466);(6.1403,2.9982,1.8513);(6.0125,2.5546,2.3631);(8.0061,2.7333,2.2811);(2.2575,1.1264,2.0222);(4.7648,1.8516,1.8413);(6.5794,2.5129,2.2954);(1.3238,1,1.5916);(3.0591,1.2468,1.74);(8.4393,42172,2.0956);(2.0353,1.6042,1.8095);(1.1326,1,1.8461);(24.479,13.553,2.6647);(1.3898,1,1.6224);(3.3405,2.02,1.8568);(4.1948,5.6048,2.0298);(2.4445,1.2899,2.148);(1.5258,1,1.7123);(2.48,2.1267,1.9237);(3.0141,1.2501,1.816);(2.7176,1.0154,1.7487);(2.3529,1.0927,1.7606);(1.2302,1,1.8041);(1.4374,1.0185,2.0309);(14.627,4.4815,2.4244);(11.488,5.5326,2.6088);(4.3238,2.1148,2.064);(1.6816,1.0306,1.9641);(5.2869,1.6521,1.4901);(3.3554,1.1951,1.7503):(3.6202.2.0078,2.1765);(2.713,1.2021,1.7802);(6.3146,3.0908,2.216);(1.6796,1.0318,2.1328);(6.2551,2.8553,2.422);(3.8393,2.1827,2.0007);(1.1526,1,1.6921);(1.2917,1,1.6915);(2.3068,1.0671,2.0863);(3.5291,1.2608,1.9603);(3.3002,2.319,2.1972);(3.1266,1.2625,2.1376);(8.6806,3.7515,2.1991);(6.7673,2.806,2.4042);(2.2946,1.0361,1.4983);(2.278,1.0333,1.6058):(4.0957,1.6927,1.811);(1.7235,1.0525,2.0375);(1.1966,1,1.7884);(3.1703,1.0661,2.2049);(2.1535,1.0124,2.0691);(1.4588,1,1.412);(1.3099,1,1.6314);(1.2489,1,1.7544);(1.3878,1,1.735);(7.7746,3.6113,2.3546);(1.1321,1,1.9499);(2.0735,1.0842,2.0159);(4.0042,1.5293,1.5789);(1.5226,1,1.957);(7.6564,3.53,2.0595);(3.5741,1.7709,2.1734);(2.2087,1,1.7119);(7.3399,3.4652,2.4166);(2.103,1.0086,1.7336);(8.0386,2.634,2.5151);(3.7536,1.9992,1.5721);(2.7591,1.2753,2.05);(14.579,9.8703,2.6138);(1.0167,1,1.9565);(6.5493,4.1002,2.1603);(2.4334,1.3311,1.557);(1.9413,1.0172,1.9888);(16.044,10.729,2.5618);(3.2475,4.5342,2.235);(3.2543,2.0052,1.7901);(6.0475,2.342,1.4765);(10.568,4.7579,2.3792);(1.1457,1,1.7457);(15.881,11.272,2.5505);(2.1857,1.0815,1.9428);(3.1535,1.0428,2.2404);(1.8287,1,1.9481);(8.9362,5.4706,2.4035);(4.7114,1.6156,2.3126);(1.3099,1,1.6314);(9.8126,2.8813,2.4984);(8.5353,3.9028,2.3765);(1.2945,1,1.6996);(3.3784,1.0092,1.8064);(6.2723,2.5051,2.3262);(2.9177,1.3043,2.2407);(1.0322,1,1.9962);(2.4883,1.0657,1.8493);(1.079,1,1.7069);(4.1242,1.1746,2.3066);(3.1917,1.2192,1.8935);(58.463,88.935,2.9122);(5.7608,3.2743,1.7914);(4.7495,1.1039,1.5398);(1.6508,1.0586,2.0954);(1.7518,1,2.1784);(1.3538,1,1.8651);(1.374,1,1.6819);(4.2773,2.3523,2.148);(3.7821,1.1338,2.0052);(1.101,1,1.6975);(1.3034,1,1.7989);(4.3521,1.4005,2.2437);(4.0155,1.0638,2.2756);(2368,1.135,1.8017);(2.6225,1.0191,1.784);(1.0918,1,2.0298);(2.1037,1.0019,1.5992);(4.3551,1.7299,1.9496);(2.5133,1.2571,2.0154);(4.1436,3.0594,1.9532);(1.9413,1,1.7288);(3.0405,2.9976,2.0924);(2.8432,1.3447,2.0309);(7.0903,4.2616,1.8306);(1.4818,1,1.4894);(1.3033,1,1.755);(4.7464,1.6671,2.2825);(6.376,1.6303,2.2585);(1.0899,1,2.0983);(1.5226,1,1.957);(1.2906,1,1.6933);(1.8017,1,1.6319);(3.4659,1.4257,2.165);(5.2547,2.7244,2.1437);(8.3371,6.17,2.2873);(3.6851,1.5661,2.1765);(6.1031,2.6431,2.2599);(1.8517,1.0069,1.9967);(6.4718,1.0936,2.139);(2.7885,1.0982,1.7716);(19.858,7.4654,2.594);(1.2321,1,1.7188);(1.9566,1.0076,2.0806);(2.3099,1.0681,1.7818);(2.3903,1.0544,2.0517);(1.1511,1,1.8797);(8.8973,2.6562,2.2964);(4.6163,1.151,1.534);(2.2511,1.2662,1.9552);(4.4431,1.2709,2.0369);(1.8073,1.0016,1.5133);(2.9284,1.0457,2.2846);(6.1928,3.5268,2.2735);(2.3053,1.1446,1.8612);(2.3591,1.0486,2.0001);(1.1069,1,1.836);(1.8571,1,1.5035);(5.8287,2.7983,1.7082);(2.5588,1.1597,1.7952);(9.3139,6.3325,2.4275);(8.6995,7.1534,2.4877);(3.1256,2.004,1.7404);(1.1247,1,1.836);(3.6309,1.3061,2.3158);(2.0484,1.0034,1.7635);(7.2108,5.9999,2.5206);(5.6211,2.9979,1.687);(5.2267,2.2311,2.392);(2.2559,1.0637,1.6582);(1.5974,1.0093,1.9651);(1.3455,1,1.6266);(4.2769,1.1673,2.3724);(5.0103,1.7704,1.9309);(1.9577,1,1.7487);(3.0464,3.9945,1.7418);(3.1894,2.7029,1.9122);(4,6727,2.2373,2.194);(2.713,1.2021,1.7802);(5.19,2.2204,2.2866);(3.2122,1.5911,2.0916);(8.9043,2.5346,1.9962);(2.551,1.0357,1.777);(3.2408,1.1689,2.0257);(1.2934,1,1.6511);(1.198,1,1.753);(4.4624,1.9992,1.4009);(1.6209,1.0776,2.1584);(3.2616,1.3987,2.2248);(5.3209,1.9657,2.3498);(15.381,7.1406,2.2255);(3.2385,2.2197,2.1325);(3.5314,1.0191,2.202);(2.6961,1.1775,2.2324);(1.3484,1,1.5452);(4.1377,1.7455,2.1628);(1.1664,1,1.8384);(2.4759,1.0342,1.9453);(3.0716,1.3475,2.2394);(1.1486,1,1.806);(2.3457,1.1041,1.956);(2.1758,1,1.6959);(3.5275,1.0456,1.8711);(5.6884,2.4599,2.2763);(4.7457,2.1567,2.2377);(5.4312,5.2033,2.1391);

Part II: IP Values of Sinomenine-Related Genes

Gene IP value NodeRank Betweenness Closeness 581 6.1834 6.5855 1.77782.2853 596 18.265 15.978 9.3801 2.5303 6347 6.3687 6.2869 2.4204 1.96266348 5.3322 4.7031 2.6926 2.1835 6352 10.809 7.3813 8.0153 2.3418 9585.7661 5.7343 2.1411 2.2518 941 2.1418 1.9577 1 1.7487 942 2.1418 1.95771 1.7487 1027 12.133 11.173 5.5676 2.521 1278 11.793 11.816 4.27432.2992 9170 1.6419 1.3043 1 1.6992 1906 4.2158 4.1058 1.6685 1.9448 22584.0385 4.2605 1.2093 2.1889 2335 33.203 24.564 22.378 2.541 3383 3.95733.883 1.5318 2.2127 8519 1.9824 1.7413 1.0096 1.8488 3596 1.91 1.63991.0176 1.7724 3553 5.0708 4.6755 2.3195 2.2548 3569 2.0616 1.8385 1.01712.0677 3576 7.1515 5.8846 4.1141 2.2384 3714 2.2781 2.1347 1.0015 1.76334322 4.8742 4.5299 2.1871 2.0369 4792 13.31 11.538 6.961 2.5457 52285.7783 6.1318 1.6879 2.3539 5743 14.065 8.6497 11.566 2.3338 7040 17.19113.854 10.236 2.4697 7124 7.5393 7.443 2.8645 2.4081 7133 4.281 4.17381.689 2.2444 7157 102 58.463 88.935 2.9122 7412 8.2158 7.461 3.89382.1731 7422 10.992 9.8995 5.3072 2.4182 (note: all genes are indicatedby Entrez GeneID of NCBI)

Part III: IP Values of Matrine-Related Genes

Gene IP value NodeRank Betweenness Closeness 7356 1.4644 1.0724 1 1.92627157 102 58.463 88.935 2.9122 581 6.1834 6.5855 1.7778 2.2853 355 9.05187.6606 4.955 2.4754 4893 16.583 15.486 7.3526 2.6097 604 5.4139 5.80411.5112 2.3273 3265 24.805 21.456 13.027 2.6747 1026 11.776 10.919 5.31372.5295 1032 2.9654 3.0231 1.0132 2.1969 596 18.265 15.978 9.3801 2.5303567 8.4655 7.3012 4.4716 2.0768 595 16.668 17.066 5.6075 2.5557 460917.158 17.874 5.4079 2.6097 5111 17.66 16.465 7.8613 2.3003 3725 35.04929.09 19.867 2.6953 891 7.8855 8.0061 2.7333 2.2811 7015 5.2026 4.71232.4805 2.34 1019 14.143 13.166 6.3207 2.4897 5743 14.065 8.6497 11.5662.3338 6774 39.944 29.049 27.518 2.8158 6777 13.096 13.48 4.3218 2.62664089 37.634 29.616 23.256 2.729 4314 6.339 6.2773 2.3856 2.1578 83640.873 28.304 29.846 2.7015 1027 12.133 11.173 5.5676 2.521 947 1.63131.2906 1 1.6933 3674 12.773 12.104 5.4543 2.4275 5925 35.666 28.48321.546 2.6751 1029 6.8854 7.2238 2.1095 2.2777 2526 8.6439 6.147 6.12031.8097 3684 8.7155 8.1735 3.8233 2.2447 4830 12.863 11.532 6.273 2.14897076 2.6601 2.5605 1.0888 1.9433 7408 9.4162 8.7928 4.1756 2.1575 562591.4583 1.0644 1 1.7306 174 1.4808 1.0939 1 1.8322 945 1.9842 1.7518 12.1784 7040 17.191 13.854 10.236 2.4697 1277 13.361 13.499 4.7103 2.330959 5.7242 4.3833 3.6815 2.2078 960 17.682 14.131 10.669 2.4803 742210.992 9.8995 5.3072 2.4182 2258 4.0385 4.2605 1.2093 2.1889 7124 7.53937.443 2.8645 2.4081 3569 2.0616 1.8385 1.0171 2.0677 1956 54.812 40.29837.241 2.8295 1281 7.5153 8.3156 1.7904 2.2447 1285 5.6574 5.8105 1.88172.1285 4313 17.966 16.406 8.4075 2.3031 2335 33.203 24.564 22.378 2.5414035 26.782 18.001 20.205 2.5557 3383 3.9573 3.883 1.5318 2.2127

Part IV: IP Values of Honokiol-Related Genes

Gene IP value NodeRank Betweenness Closeness 7157 102 58.463 88.9352.9122 7124 7.5393 7.443 2.8645 2.4081 3586 1.5605 1.198 1 1.753 59813.987 13.585 5.5789 2.458 836 40.873 28.304 29.846 2.7015 6647 5.62795.0143 2.7821 2.1921 5443 3.0752 2.5093 1.7942 2.1747 581 6.1834 6.58551.7778 2.2853 596 18.265 15.978 9.3801 2.5303 796 3.1253 3.243 1.00011.3464 6750 3.2638 3.2758 1.1763 1.7635 4842 7.5971 6.9663 3.5209 2.2653553 5.0708 4.6755 2.3195 2.2548 4353 3.1482 3.0533 1.2612 1.7463 574314.065 8.6497 11.566 2.3338 3791 15.49 15.469 5.6757 2.5692 4318 16.36615.381 7.1406 2.2255 4843 6.6256 5.8787 3.3044 2.2825

Part V: IP Values of Luteolin-Related Genes

Gene IP value NodeRank Betweenness Closeness 207 41.581 30.857 27.9112.7529 367 45.012 29.003 35.443 2.8493 581 6.1834 6.5855 1.7778 2.2853596 18.265 15.978 9.3801 2.5303 598 13.987 13.585 5.5789 2.458 83640.873 28.304 29.846 2.7015 841 17.184 15.391 8.3992 2.4836 891 7.88558.0061 2.7333 2.2811 595 16.668 17.066 5.6075 2.5557 958 5.7661 5.73432.1411 2.2518 1020 11.262 8.6995 7.1534 2.4877 1026 11.776 10.919 5.31372.5295 1576 4.8152 4.8053 1.7683 1.8434 1906 4.2158 4.1058 1.6685 1.94481956 54.812 40.298 37.241 2.8295 355 9.0518 7.6606 4.955 2.4754 235316.587 16.151 6.5691 2.5419 2308 7.0002 5.8731 3.8929 2.4881 2309 4.86375.1355 1.4512 2.4042 3480 28.588 22.918 17.166 2.6563 3596 1.91 1.63991.0176 1.7724 3565 1.552 1.1869 1 1.6451 3569 2.0616 1.8385 1.01712.0677 3576 7.1515 5.8846 4.1141 2.2384 3725 35.049 29.09 19.867 2.69535594 65.085 45.803 46.655 2.9603 1432 28.895 23.829 16.561 2.6671 559925.144 21.539 13.456 2.7049 4792 13.31 11.538 6.961 2.5457 4843 6.62565.8787 3.3044 2.2825 142 8.5803 7.8752 3.9678 2.3173 5335 26.35 24.98911.23 2.5846 9536 7.8641 6.069 5.0018 1.8073 5743 14.065 8.6497 11.5662.3338 6774 39.944 29.049 27.518 2.8158 7124 7.5393 7.443 2.8645 2.40817128 2.9995 3.0067 1.0856 1.9983 7157 102 58.463 88.935 2.9122 742210.992 9.8995 5.3072 2.4182

Part VI: Matrix of Shortest Paths Among Sinomenine-Matrine—Related Genes

4, 4, 4, 4, 3, 4, 5, 5, 3, 2, 4, 4, 4, 1, 4, 4, 4, 4, 4, 3, 4, 3, 4, 4, 4, 3, 3, 4, 3, 3, 31, 1, 3, 3, 3, 2, 4, 4, 2, 3, 4, 3, 3, 2, 3, 2, 4, 3, 3, 2, 3, 3, 1, 2, 1, 2, 2, 3, 0, 3, 20, 1, 4, 4, 3, 3, 4, 4, 2, 3, 5, 4, 3, 3, 3, 3, 4, 3, 4, 3, 4, 4, 2, 3, 2, 3, 3, 4, 1, 3, 32, 2, 3, 3, 3, 3, 3, 3, 2, 3, 4, 4, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 2, 2, 2, 3, 3, 3, 1, 3, 22, 1, 4, 2, 2, 3, 4, 4, 2, 3, 3, 3, 2, 2, 2, 3, 3, 2, 2, 2, 3, 3, 2, 2, 3, 2, 2, 3, 2, 2, 23, 2, 4, 2, 2, 3, 4, 4, 3, 4, 5, 3, 3, 3, 3, 4, 5, 2, 2, 2, 4, 4, 2, 3, 3, 3, 2, 3, 2, 4, 32, 1, 4, 2, 2, 2, 3, 3, 2, 3, 3, 3, 2, 3, 2, 3, 3, 2, 2, 2, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 22, 2, 3, 3, 3, 3, 4, 4, 2, 3, 4, 3, 3, 3, 3, 3, 4, 3, 3, 3, 4, 4, 2, 2, 2, 3, 2, 3, 1, 3, 23, 3, 4, 3, 3, 3, 4, 4, 2, 3, 5, 3, 4, 2, 4, 4, 5, 3, 3, 3, 3, 4, 3, 3, 3, 2, 3, 3, 2, 4, 31, 0, 4, 3, 3, 3, 4, 4, 2, 3, 4, 3, 3, 3, 3, 3, 4, 3, 3, 2, 3, 4, 2, 3, 2, 3, 2, 3, 1, 3, 34, 3, 4, 4, 4, 4, 4, 4, 3, 3, 5, 2, 4, 3, 4, 4, 5, 2, 4, 4, 4, 3, 3, 3, 4, 3, 4, 4, 3, 4, 33, 2, 3, 2, 2, 3, 3, 3, 1, 4, 4, 3, 3, 3, 3, 4, 4, 2, 2, 2, 3, 4, 2, 2, 3, 3, 2, 2, 2, 3, 23, 2, 3, 3, 3, 3, 3, 3, 2, 3, 4, 3, 3, 3, 3, 4, 4, 3, 3, 3, 3, 3, 3, 2, 3, 2, 2, 2, 2, 3, 23, 2, 4, 3, 3, 2, 4, 4, 2, 3, 5, 3, 4, 3, 4, 4, 4, 3, 3, 3, 4, 4, 2, 3, 3, 3, 3, 3, 2, 3, 33, 2, 3, 1, 1, 3, 4, 4, 2, 3, 4, 3, 3, 2, 3, 4, 4, 1, 1, 1, 3, 4, 2, 2, 3, 2, 1, 2, 2, 3, 22, 3, 4, 3, 3, 3, 4, 4, 1, 3, 4, 4, 3, 3, 3, 4, 4, 3, 3, 3, 4, 3, 3, 3, 3, 2, 3, 3, 2, 4, 32, 3, 4, 4, 3, 2, 4, 4, 2, 3, 4, 4, 3, 3, 3, 4, 4, 3, 3, 4, 4, 3, 2, 3, 3, 3, 2, 3, 2, 3, 33, 2, 4, 3, 3, 3, 4, 4, 1, 3, 4, 2, 3, 3, 3, 4, 4, 3, 3, 3, 3, 4, 3, 3, 3, 2, 2, 3, 2, 3, 32, 2, 4, 4, 4, 2, 4, 4, 3, 3, 4, 3, 3, 3, 3, 3, 4, 4, 4, 3, 4, 3, 2, 3, 0, 3, 2, 2, 1, 3, 33, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 2, 2, 3, 3, 2, 2, 2, 4, 3, 2, 1, 3, 3, 2, 2, 2, 3, 12, 2, 3, 3, 3, 3, 2, 2, 2, 3, 4, 3, 2, 3, 2, 3, 3, 3, 3, 3, 4, 3, 2, 2, 3, 3, 2, 2, 2, 3, 23, 2, 3, 2, 2, 3, 4, 4, 1, 2, 4, 3, 3, 1, 3, 4, 4, 2, 2, 2, 2, 3, 2, 3, 3, 1, 2, 3, 2, 3, 33, 3, 1, 3, 3, 3, 5, 5, 3, 2, 4, 4, 3, 2, 3, 4, 4, 3, 4, 3, 4, 2, 2, 3, 3, 2, 2, 3, 2, 3, 32, 1, 4, 3, 3, 2, 3, 3, 2, 2, 4, 3, 2, 2, 3, 4, 4, 2, 3, 3, 3, 3, 3, 2, 3, 2, 2, 2, 2, 3, 22, 2, 4, 3, 2, 3, 3, 3, 0, 3, 4, 3, 3, 2, 3, 4, 4, 3, 3, 3, 3, 4, 2, 3, 3, 2, 3, 3, 2, 3, 34, 4, 4, 4, 3, 5, 5, 5, 3, 4, 5, 4, 4, 4, 4, 5, 5, 4, 5, 4, 6, 5, 4, 4, 5, 4, 4, 5, 4, 4, 43, 3, 3, 3, 2, 3, 4, 4, 2, 1, 4, 4, 3, 1, 3, 4, 4, 3, 4, 3, 4, 2, 3, 3, 3, 2, 3, 3, 2, 3, 23, 2, 4, 2, 2, 2, 4, 4, 2, 3, 4, 3, 3, 2, 3, 4, 3, 2, 2, 2, 4, 4, 2, 3, 3, 3, 2, 3, 2, 2, 33, 3, 4, 3, 3, 3, 4, 4, 2, 3, 4, 3, 3, 2, 4, 4, 5, 3, 3, 3, 3, 4, 3, 2, 3, 2, 3, 3, 2, 4, 24, 4, 3, 4, 3, 4, 5, 5, 4, 4, 5, 5, 4, 4, 2, 3, 5, 5, 5, 5, 6, 4, 4, 4, 4, 4, 4, 4, 3, 4, 43, 3, 3, 3, 3, 3, 4, 4, 3, 3, 4, 3, 3, 3, 1, 3, 4, 4, 4, 4, 5, 3, 3, 3, 3, 3, 3, 3, 2, 3, 33, 3, 3, 4, 3, 3, 5, 5, 3, 2, 4, 4, 3, 2, 4, 4, 3, 4, 4, 4, 5, 3, 3, 3, 3, 3, 4, 3, 2, 2, 34, 4, 2, 3, 4, 4, 5, 5, 4, 2, 4, 3, 3, 2, 4, 3, 4, 2, 4, 4, 3, 3, 3, 4, 4, 2, 3, 4, 3, 3, 42, 3, 3, 3, 3, 3, 4, 4, 3, 2, 4, 4, 3, 2, 3, 3, 3, 3, 4, 4, 5, 3, 3, 3, 3, 2, 4, 3, 3, 2, 34, 4, 3, 4, 4, 4, 5, 5, 4, 3, 4, 4, 4, 3, 4, 4, 4, 4, 5, 4, 6, 4, 4, 4, 4, 3, 4, 4, 4, 3, 44, 4, 2, 2, 2, 4, 4, 4, 4, 4, 5, 5, 4, 4, 4, 5, 4, 4, 4, 3, 6, 3, 3, 3, 4, 3, 4, 3, 3, 4, 33, 3, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 3, 3, 3, 4, 3, 3, 3, 4, 5, 3, 2, 3, 3, 3, 3, 3, 3, 3, 33, 3, 3, 1, 3, 3, 5, 5, 2, 2, 4, 3, 3, 2, 3, 4, 4, 2, 3, 3, 3, 2, 3, 2, 3, 0, 2, 3, 2, 3, 23, 3, 3, 3, 2, 3, 4, 4, 3, 2, 3, 4, 3, 1, 3, 3, 3, 2, 4, 2, 4, 3, 3, 3, 3, 2, 2, 3, 2, 2, 23, 3, 4, 3, 3, 4, 4, 4, 3, 2, 5, 3, 3, 2, 4, 3, 5, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 2, 4, 33, 3, 2, 2, 2, 3, 3, 3, 3, 1, 2, 4, 2, 1, 2, 4, 4, 3, 4, 2, 4, 2, 2, 2, 3, 2, 3, 3, 2, 3, 23, 3, 3, 3, 2, 3, 4, 4, 3, 2, 3, 4, 2, 2, 2, 4, 4, 3, 3, 3, 4, 3, 3, 1, 3, 2, 3, 2, 2, 3, 03, 3, 4, 4, 3, 3, 4, 4, 3, 3, 3, 4, 0, 3, 2, 4, 4, 3, 4, 3, 5, 4, 3, 2, 3, 3, 3, 3, 3, 3, 23, 2, 3, 2, 2, 3, 4, 4, 3, 2, 4, 4, 3, 2, 3, 4, 4, 2, 2, 2, 3, 3, 2, 3, 2, 2, 0, 1, 2, 3, 34, 3, 4, 2, 2, 4, 4, 4, 3, 4, 5, 4, 4, 3, 4, 5, 4, 2, 0, 2, 4, 4, 3, 3, 4, 3, 2, 3, 3, 4, 32, 2, 3, 3, 2, 2, 3, 3, 2, 2, 3, 3, 1, 2, 1, 3, 3, 2, 3, 3, 4, 3, 2, 1, 2, 2, 2, 2, 2, 2, 13, 3, 3, 3, 2, 3, 4, 4, 3, 2, 3, 3, 3, 2, 3, 3, 3, 3, 4, 2, 3, 3, 3, 3, 3, 2, 3, 3, 2, 2, 24, 3, 3, 3, 3, 3, 5, 5, 3, 2, 4, 4, 3, 1, 3, 4, 4, 3, 4, 2, 4, 3, 4, 3, 3, 2, 2, 3, 3, 3, 33, 3, 3, 2, 3, 3, 5, 5, 3, 2, 4, 4, 2, 2, 3, 4, 3, 1, 3, 3, 4, 2, 3, 3, 3, 1, 2, 3, 2, 2, 33, 3, 3, 3, 2, 3, 4, 4, 2, 1, 3, 3, 3, 0, 3, 3, 3, 3, 3, 2, 3, 2, 3, 3, 3, 2, 2, 3, 2, 2, 23, 3, 3, 3, 3, 2, 4, 4, 3, 2, 4, 3, 3, 2, 3, 3, 3, 2, 3, 3, 4, 1, 2, 3, 2, 2, 2, 2, 2, 2, 23, 3, 4, 4, 3, 3, 4, 4, 3, 3, 3, 4, 2, 3, 0, 4, 4, 3, 4, 4, 5, 4, 3, 2, 3, 3, 3, 3, 3, 3, 2

Part VII: Matrix of Shortest Paths Among Sinomenine—Honokiol—RelatedGenes

1, 1, 3, 3, 3, 2, 4, 4, 2, 3, 4, 3, 3, 2, 3, 2, 4, 3, 3, 2, 3, 3, 1, 2, 1, 2, 2, 3, 0, 3, 23, 2, 3, 2, 2, 3, 4, 4, 3, 2, 4, 4, 3, 2, 3, 4, 4, 2, 2, 2, 3, 3, 2, 3, 2, 2, 0, 1, 2, 3, 34, 4, 4, 4, 4, 4, 6, 6, 4, 3, 5, 4, 4, 3, 4, 4, 5, 2, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4, 4, 31, 1, 3, 3, 3, 3, 3, 3, 2, 3, 4, 4, 3, 3, 3, 3, 4, 2, 3, 3, 3, 4, 2, 3, 2, 3, 2, 3, 1, 3, 32, 1, 4, 3, 3, 2, 3, 3, 2, 2, 4, 3, 2, 2, 3, 4, 4, 2, 3, 3, 3, 3, 3, 2, 3, 2, 2, 2, 2, 3, 22, 1, 4, 4, 4, 3, 4, 4, 3, 4, 5, 4, 4, 3, 3, 3, 5, 4, 4, 3, 4, 4, 2, 3, 2, 3, 3, 4, 1, 4, 34, 3, 3, 3, 3, 4, 4, 4, 3, 3, 4, 4, 3, 2, 3, 4, 4, 3, 3, 3, 5, 4, 3, 2, 4, 2, 3, 3, 3, 3, 20, 1, 4, 4, 3, 3, 4, 4, 2, 3, 5, 4, 3, 3, 3, 3, 4, 3, 4, 3, 4, 4, 2, 3, 2, 3, 3, 4, 1, 3, 31, 0, 4, 3, 3, 3, 4, 4, 2, 3, 4, 3, 3, 3, 3, 3, 4, 3, 3, 2, 3, 4, 2, 3, 2, 3, 2, 3, 1, 3, 36, 6, 7, 6, 5, 6, 6, 6, 5, 6, 6, 3, 5, 5, 5, 6, 7, 5, 6, 6, 7, 6, 5, 5, 5, 5, 6, 6, 5, 6, 53, 3, 5, 5, 4, 4, 4, 4, 4, 5, 5, 5, 4, 4, 4, 4, 4, 5, 4, 4, 5, 5, 3, 4, 3, 4, 4, 4, 2, 5, 43, 2, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 3, 3, 4, 3, 3, 3, 3, 4, 4, 2, 3, 3, 3, 3, 3, 2, 3, 33, 3, 4, 2, 2, 3, 5, 5, 3, 3, 5, 4, 3, 3, 3, 4, 4, 0, 2, 2, 4, 3, 3, 3, 4, 2, 2, 3, 3, 3, 34, 4, 4, 4, 4, 4, 6, 6, 4, 4, 6, 4, 4, 4, 4, 5, 6, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 4, 5, 42, 2, 4, 4, 4, 2, 4, 4, 3, 3, 4, 3, 3, 3, 3, 3, 4, 4, 4, 3, 4, 3, 2, 3, 0, 3, 2, 2, 1, 3, 33, 3, 3, 3, 2, 2, 3, 3, 2, 2, 4, 3, 3, 2, 2, 3, 3, 3, 3, 3, 4, 2, 2, 1, 2, 3, 3, 2, 2, 2, 13, 3, 3, 3, 3, 4, 4, 4, 3, 1, 3, 4, 3, 1, 3, 4, 3, 3, 3, 1, 4, 2, 3, 3, 3, 2, 3, 4, 2, 2, 33, 2, 4, 3, 3, 2, 4, 4, 3, 3, 4, 3, 3, 3, 3, 4, 4, 3, 3, 3, 4, 3, 2, 3, 2, 3, 3, 2, 3, 3, 3

Part VIII: Matrix of Shortest Paths Among Sinomenine—Luteolin—RelatedGenes

2, 2, 3, 3, 3, 2, 4, 4, 1, 2, 4, 3, 3, 2, 3, 4, 3, 3, 3, 3, 4, 3, 2, 2, 3, 3, 2, 2, 2, 2, 22, 2, 3, 2, 2, 2, 4, 4, 2, 3, 4, 3, 2, 2, 2, 4, 4, 2, 2, 2, 3, 3, 2, 2, 2, 2, 2, 2, 2, 3, 20, 1, 4, 4, 3, 3, 4, 4, 2, 3, 5, 4, 3, 3, 3, 3, 4, 3, 4, 3, 4, 4, 2, 3, 2, 3, 3, 4, 1, 3, 31, 0, 4, 3, 3, 3, 4, 4, 2, 3, 4, 3, 3, 3, 3, 3, 4, 3, 3, 2, 3, 4, 2, 3, 2, 3, 2, 3, 1, 3, 31, 1, 3, 3, 3, 3, 3, 3, 2, 3, 4, 4, 3, 3, 3, 3, 4, 2, 3, 3, 3, 4, 2, 3, 2, 3, 2, 3, 1, 3, 32, 1, 4, 3, 3, 2, 3, 3, 2, 2, 4, 3, 2, 2, 3, 4, 4, 2, 3, 3, 3, 3, 3, 2, 3, 2, 2, 2, 2, 3, 22, 1, 4, 3, 3, 2, 3, 3, 1, 2, 4, 4, 3, 3, 3, 4, 4, 3, 3, 3, 3, 3, 2, 3, 3, 2, 2, 2, 2, 3, 32, 3, 4, 3, 3, 3, 4, 4, 1, 3, 4, 4, 3, 3, 3, 4, 4, 3, 3, 3, 4, 3, 3, 3, 3, 2, 3, 3, 2, 4, 33, 2, 3, 2, 2, 3, 3, 3, 1, 4, 4, 3, 3, 3, 3, 4, 4, 2, 2, 2, 3, 4, 2, 2, 3, 3, 2, 2, 2, 3, 23, 3, 4, 4, 4, 0, 4, 4, 3, 3, 4, 3, 3, 3, 3, 4, 4, 3, 4, 4, 4, 3, 3, 3, 2, 3, 3, 2, 2, 3, 32, 2, 4, 3, 3, 3, 3, 3, 1, 2, 4, 4, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 2, 3, 2, 2, 3, 3, 1, 3, 32, 2, 3, 3, 3, 3, 4, 4, 2, 3, 4, 3, 3, 3, 3, 3, 4, 3, 3, 3, 4, 4, 2, 2, 2, 3, 2, 3, 1, 3, 24, 3, 5, 4, 4, 4, 5, 5, 4, 4, 4, 4, 4, 3, 3, 5, 5, 4, 4, 4, 5, 5, 4, 4, 2, 4, 2, 4, 3, 4, 44, 3, 5, 4, 4, 3, 4, 4, 3, 3, 4, 0, 4, 3, 4, 4, 5, 4, 4, 4, 4, 4, 3, 4, 3, 3, 4, 3, 3, 4, 42, 2, 3, 3, 2, 2, 3, 3, 2, 2, 3, 3, 1, 2, 1, 3, 3, 2, 3, 3, 4, 3, 2, 1, 2, 2, 2, 2, 2, 2, 12, 2, 3, 3, 3, 3, 3, 3, 2, 3, 4, 4, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 2, 2, 2, 3, 3, 3, 1, 3, 23, 2, 3, 1, 1, 3, 4, 4, 2, 3, 4, 4, 3, 3, 3, 4, 4, 1, 1, 1, 3, 4, 2, 2, 3, 2, 1, 2, 2, 3, 23, 3, 3, 3, 3, 3, 4, 4, 2, 3, 4, 4, 3, 2, 3, 4, 4, 3, 3, 3, 3, 3, 2, 3, 3, 2, 3, 3, 2, 3, 33, 3, 4, 3, 3, 3, 4, 4, 2, 3, 4, 3, 3, 2, 3, 4, 4, 3, 3, 3, 3, 4, 2, 3, 3, 2, 2, 3, 2, 3, 33, 2, 3, 3, 2, 3, 3, 3, 2, 2, 2, 3, 1, 2, 2, 3, 3, 3, 3, 3, 4, 3, 2, 1, 3, 3, 3, 3, 2, 2, 14, 4, 4, 4, 4, 4, 4, 4, 4, 3, 5, 5, 4, 3, 4, 4, 0, 4, 4, 4, 6, 4, 3, 4, 4, 4, 4, 4, 4, 1, 44, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 4, 4, 4, 5, 2, 5, 4, 5, 6, 5, 3, 4, 4, 5, 4, 4, 4, 3, 44, 3, 4, 2, 2, 4, 4, 4, 3, 4, 5, 4, 4, 3, 4, 5, 4, 2, 0, 2, 4, 4, 3, 3, 4, 3, 2, 3, 3, 4, 33, 2, 2, 2, 2, 4, 5, 5, 3, 2, 4, 4, 3, 2, 4, 4, 4, 2, 2, 0, 4, 3, 3, 3, 3, 3, 2, 3, 2, 3, 33, 2, 3, 1, 1, 3, 4, 4, 2, 3, 4, 3, 3, 2, 3, 4, 4, 1, 1, 1, 3, 4, 2, 2, 3, 2, 1, 2, 2, 3, 22, 1, 3, 2, 2, 2, 3, 3, 2, 3, 4, 3, 2, 2, 2, 3, 4, 2, 2, 2, 3, 3, 2, 2, 2, 2, 1, 2, 1, 3, 22, 1, 3, 2, 2, 3, 4, 4, 2, 3, 4, 4, 2, 3, 3, 3, 3, 2, 2, 1, 3, 4, 2, 2, 2, 2, 2, 2, 1, 2, 22, 1, 3, 2, 2, 3, 4, 4, 2, 3, 3, 3, 3, 3, 2, 3, 4, 2, 2, 2, 3, 3, 2, 2, 2, 2, 1, 2, 1, 3, 22, 2, 3, 3, 3, 3, 3, 3, 2, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 0, 3, 2, 3, 2, 3, 1, 3, 33, 2, 4, 3, 3, 2, 4, 4, 3, 3, 4, 3, 3, 3, 3, 4, 4, 3, 3, 3, 4, 3, 2, 3, 2, 3, 3, 2, 3, 3, 32, 1, 4, 4, 3, 2, 4, 4, 3, 3, 5, 4, 3, 3, 3, 3, 3, 2, 4, 3, 4, 3, 2, 3, 2, 3, 3, 4, 1, 2, 33, 2, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 1, 3, 3, 3, 3, 3, 5, 3, 2, 2, 2, 3, 3, 3, 2, 2, 23, 3, 5, 5, 5, 3, 5, 5, 4, 4, 5, 4, 4, 4, 4, 4, 5, 5, 5, 4, 5, 4, 3, 4, 1, 4, 3, 3, 2, 4, 42, 2, 4, 4, 4, 2, 4, 4, 3, 3, 4, 3, 3, 3, 3, 3, 4, 4, 4, 3, 4, 3, 2, 3, 0, 3, 2, 2, 1, 3, 33, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 2, 2, 3, 3, 2, 2, 2, 4, 3, 2, 1, 3, 3, 2, 2, 2, 3, 13, 2, 3, 2, 2, 3, 4, 4, 3, 2, 4, 4, 3, 2, 3, 4, 4, 2, 2, 2, 3, 3, 2, 3, 2, 2, 0, 1, 2, 3, 34, 3, 4, 4, 4, 2, 4, 4, 3, 4, 5, 4, 4, 4, 4, 5, 4, 3, 4, 4, 5, 4, 2, 4, 3, 3, 2, 2, 3, 4, 41, 1, 3, 3, 3, 2, 4, 4, 2, 3, 4, 3, 3, 2, 3, 2, 4, 3, 3, 2, 3, 3, 1, 2, 1, 2, 2, 3, 0, 3, 23, 3, 3, 3, 2, 3, 4, 4, 3, 2, 3, 4, 2, 2, 2, 4, 4, 3, 3, 3, 4, 3, 3, 1, 3, 2, 3, 2, 2, 3, 0

Embodiment 2 Determination of Effects on Anti Blood Vessel EndothelialCell Migration of Six Medicines, Based on Angiogenesis Network

Experiment: applying the above-described method of the invention fordetermination of medicine action based on gene network to the screeningof matrine and five other anti-Angiogenesis medicines (as mainingredients of QLY), and effects of the six medicines were basicallydetermined with respect to the bioprocess of endothelial cell migrationby evaluating correlation between a medicine (medicine-related genesubset) and a bioprocess (bioprocess-related gene subset), as indicatedby equation (1)

$\begin{matrix}{\; {{ST} = {\frac{1}{2} \times \left( {\frac{\begin{matrix}{\sum\limits_{i}{{{VC}_{D}(i)} \times}} \\{\exp \left( {- {\min \left( d_{i,j} \right)}} \right)}\end{matrix}}{\sum\limits_{i}{{VC}_{D}(i)}} + \frac{\begin{matrix}{\sum\limits_{j}{{{VC}_{G}(j)} \times}} \\{\exp \left( {- {\min \left( d_{i,j} \right)}} \right)}\end{matrix}}{\sum\limits_{j}{{VC}_{G}(j)}}} \right)}}} & {{Equ}.\mspace{14mu} (1)}\end{matrix}$

where ST (Score of Topology) was score of correlation between the subsetof genes/gene products of a medicine action and the subset of genes/geneproducts of a bioprocess (such as endothelial cell migration), VC(Vertex Centrality) was the importance of the genes/gene products in thesubset of genes/gene products of the medicine action or bioprocess inthe network, where VC_(D)(i) was the node importance score of node icorresponding to genes/gene products in the subset of genes/geneproducts of a medicine action, and VC_(G)(j) was the node importancescore of node j corresponding to genes/gene products in the subset ofgenes/gene products of a bioprocess. d_(i,j) and d_(j,i) were theshortest path from node i to node j and from node j to node irespectively.

A basic point of the method according to the present embodiment is todetermine the topological correlation (including, such as, the length ofthe shortest path d between network nodes, and etc.) of the followingtwo group of genes/gene products on a disease-related gene network (suchas the above-mentioned Angiogenesis network):

-   -   a group of genes and/or gene products relating to a medicine        action to be determined (a subset of medicine-related genes/gene        products), and    -   a group of genes and/or gene products relating to a bioprocess        in a disease (a subset of bioprocess-related genes/gene        products).

According to an embodiment of the present invention, this determinationincluded measuring the importance (indicated by VC) of a related node(gene/gene product) in the network (such as whether it was a key node,whether it was a node of strong connectivity, and etc.);

And the ST score expressed in Equ. (1) was obtained.

This score was used for ranking of the extent of correlation between amedicine action and a bioprocess, and for determining/predicting theeffect of the medicine/component involved by comparison with ST scoresof other medicines/components.

FIG. 3 is a schematic flowchart of an embodiment of the method. As aspecific example, given a bioprocess participated by m genes/geneproducts, and n candidate medicine or components (Drug₁, . . . ,Drug_(n)).

Subset of genes/gene products corresponding to endothelial cellmigration can be obtained from biological experiment or database like GO(http://www.geneontologv.org/); subset of genes/gene relating to amedicine action can be obtained/improved by literature mining/collectionin such as PubMed, CNKI, etc., or by Docking, biological experimentsand/or etc.

Since the NIDA method of the invention has good robustness, differencein the subsets of genes/gene products used had relatively small effecton the outcomes of application of the NIDA method or was evennegligible, as explained above.

Then, genes/gene products in the subset of genes/gene productscorresponding to a bioprocess (such as endothelial cell migration) inAngiogenesis and genes/gene products in the subset of genes/geneproducts of each medicine were mapped onto the Angiogenesis gene network(which is published at and can be downloaded from:http://bioinfo.au.tsinghua.edu.cn/member/nzhang/download/angiogenesis.txt).

According to the definitions of NodeRank, Betweenness and Closeness, asexplained above, their values for each of the nodes were calculated;and, to ensure comparability of data, these values were normalized bydividing respective minimum value. Finally, a 1893*3 matrix A wasobtained, as given in “Part I: Noderank-Betweenness-Closeness values ofAngiogenesis Network (Matrix A)”, wherein the three values in the i-thparentheses were the noderank, betweenness and closeness values of thei-th gene listed in the webpage of:

http://bioinfo.au.tsinghua.edu.cn/member/nzhang/download/angiogenesis.txt

PCA (principal components analysis) on the three 1893*1 vectors formedby the three columns of the matrix A respectively was carried out inMATLAB. The commands in MATLAB were [COEFF, SCORE]=princomp(A);VC=-A*COEFF(: 1), where the 1893*3 matrix formed by the three vectorswas labeled A and the final node importance factor vector was labeledVC. (Of course, we can use only any two of the three vectors for PCA toobtain the IP value of node; if only Betweenness and Closeness arechosen, the corresponding program in MATLAB is: [COEFF,SCORE]=princomp(C); VC=-A*COEFF(: 1), where C stands for thecorresponding 1893*2 matrix).

Thus, the ST score of each medicine relating to the correspondingdisease/treatment process was obtained on the basis of the nodeimportance and length of the shortest path of the network correspondingto the disease/treatment, and then the corresponding ranking wasobtained.

As an alternative approach, instead of mapping genes/gene products insubsets of bioprocess and medicine/component to the gene network ofcorresponding disease (such as Angiogenesis network), they can also bemapped to any known global gene network (such as human global genenetwork, or an animal's global gene network in the case of veterinarydrug research) to obtain substantially the same results ofdetermination. Obviously, all such variations are within the scope ofthe present invention.

According to an embodiment of the present invention, EC migration as abioprocess (GO number: 0043542) with close correlation to angiogenesiswas chosen, and extent of actions by Matrine, Quercetin, Emodin,Evodiamine, Genistein, and Aconitine were determined. The screening bythe NIDA method of the present invention showed (see Table 4) thatmatrine had strong correlation to the bioprocess of endothelial cellmigration and the highest ranking among the six medicines chosen;matrine had the strongest ability in inhibiting endothelial cellmigration, stronger than the other five medicine. This showed thatmatrine had remarkable effect on endothelial cell migration.

Experiments carried out by the inventors (as will be described below)supported the above results. And the ranking of inhibition ratios of thesix medicines determined by the experiments were basically the same asthat determined by NIDA method, showing that NIDA truly reflected theaction of these medicines.

TABLE 4 extent of action by Matrine, Quercetin, Emodin, Evodiamine,Genistein, and Aconitine on the bioprocess of endothelial cellmigration, as determined by NIDA method Ranking by NIDA experimentmedicine bioprocess RANKING results Matrine endothelial cell 1 1Quercetin migration (GO 2 2 Emodin number: 0043542; 3 3 Evodiaminelimitation: Homo 4 4 Aconitine sapiens) 5 6 Genistein 6 5

Experimental Determination of In Vitro Effect of Matrine and Etc. onEndothelial Cell Migration

Experiment: cell scratch experiments were used to quantitively measurethe inhibition of medicines on migration. The principle of theexperiment was to culture endothelial cells on a plate or in a culturedish; after cell fusion, a line was drawn with cell scraper in thecentral area so that cells within this line were mechanically removed,then the cells were further cultured and cell migration to the scrappedarea without any cell was observed for determining in vitro effect ofmedicine on endothelial cell migration. Sterile operation had to beensured during cell scrapping process. Human Umbilical Vein EndothelialCells (HUVECs) available from Cascade Biologics (Portland, USA) wererepeatably cultured with the supplier's criterions; Matrine, quercetin,emodin, evodiamine, genistein, and aconitine were purchased fromNational Institute for the Control of Pharmaceutical and BiologicalProducts, and their experimental density were set at 10 mM as indicatedby literature. HUVECs were cultured on 12-plate coated by 0.1% gelatin,the cells were inactivated after 2 hours under the conditions of 5% CO₂,37° C. and 10 ug/ml of Mitonycin C. Cell scraper about 1 mm in width wasused to scrap on the fused surface of single-layer of cells. Standardendothelial cell growth medium (ECGM) free of medicine application wastaken as control group. Pictures were taken at 6 h−10 h of medicineintervention by Nikon digital camera, with migration distance beingscaled by configure software. The experiment was repeated five times.The anti-proliferation ability of the medicines was measured byMigration Inhibition Rate, determined as:

Migration Inhibition Rate=(migration distance of control group−migrationdistance of experimental group)/migration distance of control group

Results of the experiment: as shown in FIG. 4, FIG. 4 a showed thescratch of Oh; FIG. 4 b showed the result of the control group after 9 hwithout medicine intervention, in which the scratch was seen to havereduced obviously; FIG. 4 c to FIG. 4 h showed the results of 9 hintervention by matrine, quercetin, emodin, evodiamine, genistein, andaconitine respectively; FIG. 4 i showed in histogram variance theinhibition of endothelial cell migration by each medicine. It was seenfrom the drawing that all six candidate anti-angiogenesi medicines hadinhibited endothelial cell migration to different extent, with matrinehaving the strongest inhibition on endothelial cell migration.

The experimental results proved the correctness of the results ofdetermination by NIDA method of the invention, and the extent ofinhibition of the six medicines as obtained by the experiments werebasically the same as the ranking obtained by NIDA method, showing thatNIDA method of the invention truly determined the feature of actions ofthese medicines.

Related data, parameters, and intermediate data and etc. are listed in“Part IX” to “Part XXI”

Part IX: Matrine-Related Genes and Corresponding VC_(D) Values

Gene VC_(D) value NodeRank Betweenness Closeness 7356 6.6017 5.49033.7287 2.0068 7157 6.516 5.7608 3.2743 1.7914 581 2.9385 2.8448 1.18331.9443 355 3.1638 2.9621 1.3937 1.7651 4893 1.4445 1.0464 1 2.1145 6042.841 2.4439 1.5082 1.9557 3265 5.5235 5.7118 1.7911 2.2873 1026 1.82091.5255 1.0152 1.9969 1032 3.139 3.2608 1.0003 1.2277 596 9.2204 7.95884.8626 2.2095 567 2.1989 2.0323 1 1.6681 595 2.1215 1.8709 1.0717 1.87464609 1.8993 1.6259 1.0177 1.9476 5111 4.2837 3.9742 1.9304 2.2694 37257.0488 7.0331 2.59 2.2487 891 2.4525 2.2575 1.1264 2.0222 7015 1.61281.2664 1 1.7096 1019 6.5671 6.7946 2.1253 2.3191 5743 1.9179 1.6651 11.4623 6774 4.1736 3.5526 2.2603 2.1934 6777 2.84 2.7233 1.1746 1.91394089 35.049 29.09 19.867 2.6953 4314 2.4546 2.2816 1.1009 1.9476 8361.5869 1.2325 1 1.7449 1027 2.8998 2.8353 1.1343 1.9086 947 2.02241.8017 1 1.6319 3674 4.6729 4.5125 1.8952 2.2926 5925 3.6487 3.88511.0501 1.8328 1029 4.2205 4.1946 1.5703 1.9967 2526 1.6516 1.3171 11.6398 3684 13.089 11.781 6.3283 2.2585 4830 2.0271 1.8071 1.0009 2.04647076 1.6652 1.3349 1 1.7245 7408 7.4038 6.9226 3.2726 2.2457 562591.4806 1.0936 1 2.0655 174 8.3541 8.4539 2.9287 2.4081 945 1.6797 1.35381 1.8651 7040 5.4139 5.8041 1.5112 2.3273 1277 4.6982 4.8684 1.51162.2954 59 2.1208 1.9222 1.0097 2.0898 960 5.0708 4.6755 2.3195 2.25487422 3.8873 4.0189 1.2617 1.6696 2258 2.6445 2.6047 1.0119 1.9499 71243.3173 3.3289 1.1963 1.2127 3569 3.1151 3.2297 1.0001 1.4774 1956 3.22443.1873 1.2203 1.9064 1281 2.6362 2.4772 1.1507 1.9406 1285 3.4975 3.36241.4363 1.8436 4313 2.2781 2.1347 1.0015 1.7633 2335 1.5817 1.2257 11.7447 4035 2.9762 2.873 1.2083 2.0249 3383 4.8623 4.5834 2.105 2.0869

Part X: Quercetin-Related Genes and Corresponding VC_(D) Values

Gene VC_(D) value NodeRank Betweenness Closeness 207 1.7431 1.43251.0049 1.9451 572 2.7095 2.1094 1.7015 1.8391 581 2.9385 2.8448 1.18331.9443 598 17.66 16.465 7.8613 2.3003 836 1.5869 1.2325 1 1.7449 8412.4022 2.2803 1.0211 1.6005 6347 2.3159 2.1177 1.0803 2.1609 948 8.21386.9995 4.4391 2.4193 1026 1.8209 1.5255 1.0152 1.9969 1027 2.8998 2.83531.1343 1.9086 1277 4.6982 4.8684 1.5116 2.2954 1543 1.4298 1.0272 11.9391 1545 1.7387 1.4309 1 1.6823 1958 1.531 1.1595 1 1.8534 20642.7437 2.7444 1 1.6661 2065 4.9377 4.1876 2.6926 2.0971 2353 2.42292.2352 1.1069 2.0575 3091 2.3686 2.1857 1.0815 1.9428 3383 4.8623 4.58342.105 2.0869 3553 10.401 8.0775 6.5547 2.0519 3569 3.1151 3.2297 1.00011.4774 3576 6.5223 6.1965 2.7663 2.3947 3725 7.0488 7.0331 2.59 2.24875594 7.0537 6.7568 2.9258 1.8812 1432 3.3638 2.9068 1.7701 1.959 55992.0136 1.7081 1.0975 2.0714 4843 8.071 7.9946 3.0349 2.4935 4846 11.2628.6995 7.1534 2.4877 5743 1.9179 1.6651 1 1.4623 5894 2.2002 2.02431.0116 1.9988 6401 1.4903 1.1063 1 1.8262 6721 2.4871 2.3398 1.08231.6453 6772 3.1451 3.0199 1.296 1.5533 7124 3.3173 3.3289 1.1963 2.21277157 6.516 5.7608 3.2743 1.7914 7412 1.4657 1.0741 1 1.7231 7422 3.88734.0189 1.2617 1.6696

Part XI: Emodin-Related Genes and Corresponding VC_(D) Values

Gene VC_(D) value NodeRank Betweenness Closeness 567 2.1989 2.0323 11.6681 596 9.2204 7.9588 4.8626 2.2095 637 4.7255 4.1271 2.435 2.0737683 1.9574 1.7159 1.0009 1.873 836 1.5869 1.2325 1 1.7449 6347 2.31592.1177 1.0803 2.1609 890 1.76 1.4588 1 1.412 891 2.4525 2.2575 1.12642.0222 1017 2.4108 2.0313 1.3303 2.17 1385 2.3387 1.8236 1.4653 1.99641436 17.625 14.359 10.31 2.294 1500 2.5426 2.368 1.135 1.8017 23351.5817 1.2257 1 1.7447 2353 2.4229 2.2352 1.1069 2.0575 3065 2.92722.9205 1.0757 2.1819 3308 2.9596 2.7089 1.3776 1.9768 3586 2.0271 1.80711.0009 2.0464 3667 1.5135 1.1366 1 1.7392 3717 1.7073 1.3898 1 1.62243725 7.0488 7.0331 2.59 2.2487 3845 3.2833 3.0295 1.4993 1.4513 39321.9564 1.683 1.0385 1.9846 5606 5.7242 4.3833 3.6815 2.2078 5608 3.85963.3428 2.0221 2.1959 5594 7.0537 6.7568 2.9258 1.8812 1432 3.3638 2.90681.7701 1.959 5599 2.0136 1.7081 1.0975 2.0714 4292 2.5439 2.4356 1.05672.0849 4318 3.9071 3.2385 2.2197 2.1325 4609 1.8993 1.6259 1.0177 1.94765048 2.785 2.7529 1.0541 1.7318 5111 4.2837 3.9742 1.9304 2.2694 647146.467 5.3825 3.6477 2.2541 5290 2.9954 3.04 1.0397 2.1615 5468 2.46152.319 1.0672 2.1334 11331 1.5531 1.1883 1 1.8861 6401 1.4903 1.1063 11.8262 4087 8.1384 7.0622 4.2476 2.151 4088 1.6512 1.3166 1 1.69 664710.376 8.4847 6.0317 1.6982 6774 4.1736 3.5526 2.2603 2.1934 6890 3.12653.0063 1.2834 2.288 7040 5.4139 5.8041 1.5112 2.3273 7076 1.6652 1.33491 1.7245 7124 3.3173 3.3289 1.1963 2.2127 7157 6.516 5.7608 3.27431.7914

Part XII: Evodiamine-Related Genes and Corresponding VC_(D) Values

Gene VC_(D) value NodeRank Betweenness Closeness 2353 2.4229 2.23521.1069 2.0575 885 1.9006 1.642 1.0006 1.7429 7422 3.8873 4.0189 1.26171.6696 796 1.4995 1.1183 1 1.6637 5743 1.9179 1.6651 1 1.4623 488 4.81524.8053 1.7683 1.8434 2520 9.1725 8.4393 4.2172 2.0956 7124 3.3173 3.32891.1963 2.2127 5594 7.0537 6.7568 2.9258 1.8812 3569 3.1151 3.2297 1.00011.4774 207 1.7431 1.4325 1.0049 1.9451 581 2.9385 2.8448 1.1833 1.9443596 9.2204 7.9588 4.8626 2.2095 598 17.66 16.465 7.8613 2.3003 15431.4298 1.0272 1 1.9391 2048 4.543 4.4054 1.8208 2.3223 4843 8.071 7.99463.0349 2.4935 5243 3.2845 3.229 1.2641 2.105 1026 1.8209 1.5255 1.01521.9969 23411 1.721 1.4077 1 2.0809

Part XIII: Aconitine-Related Genes and Corresponding VC_(D) Values

Gene VC_(D) value NodeRank Betweenness Closeness 6647 10.376 8.48476.0317 1.6982 4086 1.4476 1.0505 1 1.9562 4087 8.1384 7.0622 4.24762.151 4088 1.6512 1.3166 1 1.69 4089 35.049 29.09 19.867 2.6953 40902.2881 2.1047 1.0526 1.7937 5594 7.0537 6.7568 2.9258 1.8812 5243 3.28453.229 1.2641 2.105 1392 5.5977 5.838 1.7565 1.7954

Part XIV: Genistein-Related Genes and Corresponding VC_(D) Values

Gene VC_(D) value NodeRank Betweenness Closeness 90 1.5204 1.1457 11.7457 207 1.7431 1.4325 1.0049 1.9451 836 1.5869 1.2325 1 1.7449 10282.6337 2.4422 1.1883 1.9279 1030 3.5809 2.9977 1.9992 1.6573 1385 2.33871.8236 1.4653 1.9964 2022 3.9549 3.8831 1.5281 2.1978 2064 2.7437 2.74441 1.6661 3761 1.4793 1.0918 1 2.0298 5594 7.0537 6.7568 2.9258 1.88121432 3.3638 2.9068 1.7701 1.959 5595 4.2418 3.3814 2.5698 1.9309 926116.366 15.381 7.1406 2.2255 4846 11.262 8.6995 7.1534 2.4877 4086 1.44761.0505 1 1.9562 6714 1.8803 1.5874 1.034 2.0763

Part XV: Genes Relating to Endothelial Cell Migration and CorrespondingVC_(D) Values

Note: gene products as recorded by GO0043542 (Homo sapiens) had thefollowing gene product information of VC_(G) value in angiogenesi genenetwork after redundant-removal

Gene VC_(D) value NodeRank Betweenness Closeness 4763 2.8498 2.7131.2021 1.7802 1906 6.7202 6.7117 2.4615 2.4201 7422 3.8873 4.0189 1.26171.6696 5155 2.2585 2.103 1.0086 1.7336 186 15.496 14.366 6.9949 2.432694 10.138 7.9944 6.2446 2.3473 1012 2.6677 2.5622 1.0985 1.8875 34850.308 39.493 31.203 2.8194 5340 1.7132 1.3975 1 1.6504

Part XVI: Matrix of Shortest Paths Between Genes of Matrine—EndothelialCell Migration

(3,4,3,3,5,4,6,4,3);(3,3,2,2,3,2,4,3,2);(3,4,3,3,4,3,5,4,3);(3,4,2,2,3,3,5,3,3):(1,3,2,2,3,3,4,3,3);(3,3,3,3,4,3,4,3,4);( 1,3,2,1,3,3,4,3,3);(3,3,2,3,4,3,4,3,3);(3,3,3,3,4,2,5,4,3);(2,3,3,2,4,3,4,3,3);(3,2,3,2,4,4,5,2,2);(3,3,2,3,3,3,4,3,3);(3,3,2,3,3,2,4,3,3);(3,3,3,3,3,3,5,3,3);(2,3,2,2,4,2,4,3,3);(3,4,3,3,4,2,5,3,4);(3,4,3,3,4,4,4,3,3);(3,2,3,3,4,2,4,3,3);(3,3,3,3,4,3,5,3,3);(3,3,1,2,3,3,4,3,3);(3,3,2,2,3,3,4,3,3);(2,3,3,3,4,2,4,3,3);(4,4,3,3,4,3,5,3,1);(2,3,2,2,4,3,4,2,2);(3,3,3,3,3,2,4,3,3);(4,4,4,4,4,5,6,4,4);(3,4,2,2,3,3,4,3,2);(3,3,3,3,3,3,4,3,2);(3,3,2,2,4,2,5,4,3);(4,5,4,4,4,5,6,4,3);(3,3,3,3,3,4,5,3,2);(4,4,3,3,4,4,5,4,3);(4,3,4,3,5,3,4,3,2);(4,4,3,3,4,3,5,4,3);(5,4,4,4,5,4,6,4,3);(5,5,3,4,4,4,6,5,4,);(3,4,3,3,4,4,5,3,3);(2,3,2,2,4,1,5,2,2);(2,4,2,1,4,3,4,3,2);(4,3,3,3,5,3,5,3,3);(3,4,2,2,3,2,4,3,2);(3,4,0,2,3,3,5,3,2);(3,4,2,2,3,4,5,4.3);(3,4,3,3,4,3,4,3,3);(3,4,3,3,5,3,5,4,4);(2,3,1,1,2,3,4,3,3);(2,3,2,2,4,3,5,3,2);(2,4,3,3,4,3,5,2,2);(3,4,3,2,4,2,5,2,2);(2,3,2,2,4,3,5,3,2);(2,3,2,1,3,3,4,1,2);(3,4,2,2,3,3,5,4,3)

Part XVII: Matrix of Shortest Paths Between Genes ofQuercetin—Endothelial Cell Migration

(3,3,2,3,3,3,3,3,3);(3,4,3,3,4,3,4,3,2);(3,4,3,3,4,3,5,4,3);(3,4,3,3,3,3,4,3,3);(2,3,2,2,4,3,4,2,2);(2,4,3,3,4,3,5,2,3);(4,5,3,4,4,4,4,4,2);(3,3,3,2,4,3,3,3,2);(3,3,2,3,4,3,4,3,3);(3,3,3,3,3,2,4,3,3);(2,4,2,1,4,3,4,3,2);(4,5,4,4,5,4,5,5,4);(4,5,4,4,5,4,5,5,4);(3,3,3,3,4,3,5,4,3);(3,3,1,1,2,3,4,3,3);(3,3,2,2,1,4,4,3,3);(2,4,2,2,4,2,4,3,3);(3,3,1,1,4,2,4,3,3);(3,4,2,2,3,3,5,4,3);(3,4,3,2,4,3,5,2,2);(3,4,3,3,5,3,5,4,4);(3,4,3,3,4,3,5,3,2);(2,3,2,2,4,2,4,3,3);(2,3,2,2,3,2,3,2,3);(3,4,2,3,3,2,4,3,3);(2,3,2,2,4,2,3,3,3);(2,3,3,2,4,3,5,3,3);(2,2,3,2,3,3,4,3,3);(3,3,3,3,4,3,5,3,3);(2,4,2,2,4,3,4,3,3);(4,4,3,3,3,3,5,4,3);(3,3,3,3,4,2,4,3,3);(3,3,1,2,3,3,4,3,3);(3,4,3,3,4,3,4,3,3);(3,3,2,2,3,2,4,3,2);(3,4,3,3,3,4,4,3,3);(3,4,0,2,3,3,5,3,2)

Part XVIII: Matrix of Shortest Paths Between Genes of Emodin—EndothelialCell Migration

(3,2,3,2,4,4,5,2,2);(2,3,3,2,4,3,4,3,3);(3,4,3,3,4,4,5,3,3);(3,3,3,3,4,3,5,3,3);(2,3,2,2,4,3,4,2,2);(4,5,3,4,4,4,4,4,2);(4,4,3,3,4,3,5,4,3);(3,4,3,3,4,2,5,3,4);(3,3,3,3,4,2,5,4,3);(3,3,3,3,4,3,4,3,4);(3,3,3,3,3,4,5,3,3);(3,4,2,2,3,4,5,3,3);(2,3,2,2,4,3,5,3,2);(2,4,2,2,4,2,4,3,3);(3,3,2,3,4,2,4,3,3);(3,4,3,3,4,3,4,4,4);(4,4,3,2,5,4,6,2,2);(3,3,2,2,3,3,4,3,3);(3,3,2,2,3,3,4,3,3);(2,3,2,2,4,2,4,3,3);(1,3,2,2,3,3,4,3,3);(3,3,2,3,3,3,4,3,3);(3,4,3,3,4,3,5,3,3);(4,4,3,3,4,3,5,3,3);(2,3,2,2,3,2,3,2,3);(3,4,2,3,3,2,4,3,3);(2,3,2,2,4,2,3,3,3);(3,4,3,3,4,3,5,3,3);(3,4,3,2,4,3,5,3,1);(3,3,2,3,3,2,4,3,3);(4,4,4,4,4,4,5,4,4);(3,3,3,3,3,3,5,3,3);(3,4,3,3,4,4,4,3,3);(2,4,2,2,2,3,3,3,3);(3,4,3,3,4,3,2,3,3);(4,4,3,3,4,3,4,4,4);(4,4,3,3,3,3,5,4,3);(3,3,3,3,4,1,4,3,2);(2,3,2,2,4,1,4,3,2);(2,3,2,2,4,1,4,3,2);(3,4,3,3,4,3,5,4,3);(3,3,1,2,3,3,4,3,3);(4,4,3,3,4,3,5,4,4);(2,3,2,2,4,1,5,2,2);(4,3,4,3,5,3,4,3,2);(3,4,3,3,4,3,4,3,3);(3,3,2,2,3,2,4,3,2)

Part XIX: Matrix of Shortest Paths Between Genes ofEvodiamine—Endothelial Cell Migration(2,4,2,2,4,2,4,3,3);(4,4,4,4,6,5,7,4,2);(3,4,0,2,3,3,5,3,2);(6,3,5,5,6,6,7,6,5);(3,3,3,3,4,3,5,3,3,);(3,4,3,3,4,4,5,4,4);(4,2,4,4,4,5,6,4,2);(3,4,3,3,4,3,4,3,3);(2,3,2,2,3,2,3,2,3);(3,4,3,3,5,3,5,4,4);(3,3,2,3,3,3,3,3,3);(3,4,3,3,4,3,5,4,3);(2,3,3,2,4,3,4,3,3);(3,4,3,3,3,3,4,3,3);(4,5,4,4,5,4,5,5,4);(2,4,3,3,3,3,5,3,3);(2,3,3,2,4,3,5,3,3);(3,3,3,3,4,3,5,3,3);(3,3,2,3,4,3,4,3,3);(4,3,3,3,4,3,4,4,3)Part XX: Matrix of Shortest Paths Between Genes of Aconitine—EndothelialCell migration

(3,4,3,3,4,3,5,4,3);(3,3,3,3,4,2,4,3,3);(3,3,3,3,4,1,4,3,2);(2,3,2,2,4,1,4,3,2);(2,3,3,3,4,2,4,3,3);(3,4,3,3,4,3,4,3,4);(2,3,2,2,3,2,3,2,3);(3,3,3,3,4,3,5,3,3);(5,4,4,4,5,4,7,5,4)

Part XXI: Matrix of Shortest Paths Between Genes ofGenistein—Endothelial Cell Migration(2,4,3,3,4,2,5,4,3);(3,3,2,3,3,3,3,3,3);(2,3,2,2,4,3,4,2,2);(3,3,3,3,4,2,5,4,3);(3,3,3,3,4,2,5,4,3);(3,3,3,3,4,3,4,3,4);(3,4,3,3,5,2,6,3,3);(3,3,1,1,2,3,4,3,3);(4,5,4,4,5,5,6,4,4);(2,3,2,2,3,2,3,2,3);(3,4,2,3,3,2,4,3,3);(2,3,2,2,3,2,4,2,3);(3,3,3,3,3,3,4,3,3);(2,2,3,2,3,3,4,3,3);(3,3,3,3,4,2,4,3,3);(2,3,2,2,3,3,4,2,2)

While the invention has been described with the above embodiments, theabove description is not given for limitation of the invention. Forexample, although at least one of the two gene subsets (two processes)in the above embodiments corresponds to action of medicine, the presentinvention is not limited to this. In the situation that all the two genesubsets/processes arepathologic/physiological/pharmacological/toxic/side-effect processes,the present invention can be implemented as a method for determining themechanism of and/or relationship among thepathologic/physiological/pharmacological/toxic/side-effect processes.Such variations, as well as other changes, modifications, andimprovements, are within the scope of the present invention.

1. a method for determining a medicine action on the basis of genenetwork, characterized by comprising: determining at least one networktopological attribute of a first subset of genes/gene products withrespect to a second subset of genes/gene products in a gene network,where said first subset of genes/gene products includes genes/geneproducts relating to a first medicine, said second subset of genes/geneproducts include genes/gene products relating to at least one of thefollowing: a second medicine, and a bioprocess.
 2. a method as claimedin claim 1, characterized in that said at least one network topologicalattribute comprises: the length of the shortest network path in saidgene network between said first subset of genes/gene products and saidsecond subset of genes/gene products, and node importance of genes/geneproducts of said first subset of genes/gene products and/or genes/geneproducts of said second subset of genes/gene products.
 3. a method asclaimed in claim 2, characterized by comprising performing comprehensiveprocessing to at least one parameter in the following parameters of eachof at least some of the nodes in said gene network so as to obtain anode importance factor (IP, VC) indicating the importance of saidnode:—a parameter (NodeRank) indicating an eigenvector corresponding tothe maxiumu eigenvalue of a network-associated matrix of said genenetwork, a parameter (Betweenness) indicating the number of shortestpaths among all genes in said gene network which go through said node,and a parameter (Closeness) indicating the inverse of the sum of theshortest paths from said node to each of all other nodes in the network.4. a method as claimed in claim 2, wherein said gene network is oneselected from: a network relating to a disease constructed on the basisof literature information and/or enome, transcriptome, proteome, and/ormetabolomics data relating to said disease, a network formed by all thenetwork relationships existing in a public protein-protein interactionnetwork database, and a network formed by all the network relationshipsexisting in a public pathway database.
 5. a method as claimed in claim2, characterized by further comprising: constructing a disease-relatedgene network of said disease, comprising: for each pair/group ofgenes/gene products in a known database of genes/gene products,determining whether all genes/gene products in said pair/group ofgenes/gene products are included in a set of genes/gene productsrelating to said disease obtained by analysis based on literature and/orexperimental information, and when all genes in said pair/group appearin said set of genes/gene products relating to said disease, determiningthat the genes in said pair/group of genes are adjacent genes in saiddisease-related gene network.
 6. a method as claimed in claim 3,characterized by that said comprehensive processing comprises principalcomponents analysis.
 7. a method as claimed in claim 2, characterized inthat the shortest network path is determined by using Floyd Algorithm,and wherein said parameter (NodeRank) indicating an eigenvectorcorresponding to the maxiumu eigenvalue of a network-associated matrixof said gene network indicates:${P(A)} = {\frac{1 - d}{N} + {d{\sum\limits_{v \in L_{v}}\frac{P(v)}{N_{v}}}}}$where N is the number of all nodes in the network, d is an attenuatingfactor smaller than 1, which indicates uncertainty of edges in thenetwork, L_(v) is the set of nodes which directly connects to node A,and N_(v) is the number of edges of node v; said parameter (Betweenness)indicating the number of shortest paths among all nodes in said genenetwork which go through said gene indicates${C_{B}(w)} = {\sum\limits_{s \in V}{\sum\limits_{{d \neq s} \in V}\frac{\sigma_{sd}(w)}{\sigma_{sd}}}}$where σ_(sd) is the number of shortest paths between any two nodes insaid gene network, σ_(sd)(w) is the number of paths among said shortestpaths which go through node w, and V is the set of nodes in said networkwhich connects to node v, and said parameter (Closeness) indicating theinverse of the sum of the shortest paths from said gene to each of allother genes in the network indicates:${C(v)} = \frac{1}{\sum\limits_{t \in V}d_{v,t}}$ where V is the setof nodes in said network which connects to node v, d_(v,t) is theshortest path from node v to node t.
 8. a method as claimed in claim 2,characterized by using a value indicating a normalized weighted averageof said node of said node importance factor (IP, VC) to characterizesaid network topological attribute, wherein the weight of said weightedaverage is inversely correlated with the minimum of the length of theshortest network path in said gene network between said first subset ofgenes/gene products and said second subset of genes/gene products.